#TensorRT

[govindhtech]

Rekor Uses NVIDIA AI Technology For Traffic Management

Rekor Uses NVIDIA Technology for Traffic Relief and Roadway Safety as Texas Takes in More Residents.

For Texas and Philadelphia highways, the company is using AI-driven analytics utilizing NVIDIA AI, Metropolis, and Jetson, which might lower fatalities and enhance quality of life.

Jobs, comedy clubs, music venues, barbecues, and more are all attracting visitors to Austin. Traffic congestion, however, are a major city blues that have come with this growth.

Due to the surge of new inhabitants moving to Austin, Rekor, which provides traffic management and public safety analytics, gets a direct view of the growing traffic. To assist alleviate the highway issues, Rekor collaborates with the Texas Department of Transportation, which is working on a $7 billion initiative to remedy this.

Based in Columbia, Maryland, Rekor has been using NVIDIA Jetson Xavier NX modules for edge AI and NVIDIA Metropolis for real-time video understanding in Texas, Florida, Philadelphia, Georgia, Nevada, Oklahoma, and many other U.S. locations, as well as Israel and other countries.

Metropolis is a vision AI application framework for creating smart infrastructure. Its development tools include the NVIDIA DeepStream SDK, TAO Toolkit, TensorRT, and NGC catalog pretrained models. The tiny, powerful, and energy-efficient NVIDIA Jetson accelerated computing platform is ideal for embedded and robotics applications.

Rekor’s initiatives in Texas and Philadelphia to use AI to improve road management are the most recent chapter in a long saga of traffic management and safety.

Reducing Rubbernecking, Pileups, Fatalities and Jams

Rekor Command and Rekor Discover are the two primary products that Rekor sells. Traffic control centers can quickly identify traffic incidents and areas of concern using Command, an AI-driven software. It provides real-time situational awareness and notifications to transportation authorities, enabling them to maintain safer and less congested municipal roads.

Utilizing Rekor’s edge technology, discover completely automates the collection of thorough vehicle and traffic data and offers strong traffic analytics that transform road data into quantifiable, trustworthy traffic information. Departments of transportation may better plan and carry out their next city-building projects by using Rekor Discover, which gives them a comprehensive picture of how cars travel on roads and the effect they have.

Command has been spread around Austin by the corporation to assist in problem detection, incident analysis, and real-time response to traffic activities.

Rekor Command receives a variety of data sources, including weather, linked vehicle information, traffic camera video, construction updates, and data from third parties. After that, it makes links and reveals abnormalities, such as a roadside incident, using AI. Traffic management centers receive the data in processes for evaluation, verification, and reaction.

As part of the NVIDIA AI Enterprise software platform, Rekor is embracing NVIDIA’s full-stack accelerated computing for roadway intelligence and investing heavily in NVIDIA AI and NVIDIA AI Blueprints, reference workflows for generative AI use cases constructed with NVIDIA NIM microservices. NVIDIA NIM is a collection of user-friendly inference microservices designed to speed up foundation model installations on any cloud or data center while maintaining data security.

Rekor is developing AI agents for municipal services, namely in areas like traffic control, public safety, and infrastructure optimization, leveraging the NVIDIA AI Blueprint for video search and summarization. In order to enable a variety of interactive visual AI agents that can extract complicated behaviors from vast amounts of live or recorded video, NVIDIA has revealed a new AI blueprint for video search and summarization.

Philadelphia Monitors Roads, EV Charger Needs, Pollution

The Philadelphia Industrial Development Corporation (PIDC), which oversees the Philadelphia Navy Yard, a famous tourist destination, has difficulties managing the roads and compiling information on new constructions. According to a $6 billion rehabilitation proposal, the Navy Yard property will bring thousands of inhabitants and 12,000 jobs with over 150 firms and 15,000 workers on 1,200 acres.

PIDC sought to raise awareness of how road closures and construction projects influence mobility and how to improve mobility during major events and projects. PIDC also sought to improve the Navy Yard’s capacity to measure the effects of speed-mitigating devices placed across dangerous sections of road and comprehend the number and flow of car carriers or other heavy vehicles.

In order to handle any fluctuations in traffic, Discover offered PIDC information about further infrastructure initiatives that must be implemented.

By knowing how many electric cars are coming into and going out of the Navy Yard, PIDC can make informed decisions about future locations for the installation of EV charging stations. Navy Yard can better plan possible locations for EV charge station deployment in the future by using Rekor Discover, which gathers data from Rekor’s edge systems which are constructed with NVIDIA Jetson Xavier NX modules for powerful edge processing and AI to understand the number of EVs and where they’re entering and departing.

By examining data supplied by the AI platform, Rekor Discover allowed PIDC planners to produce a hotspot map of EV traffic. The solution uses Jetson and NVIDIA’s DeepStream data pipeline for real-time traffic analysis. To further improve LLM capabilities, it makes advantage of NVIDIA Triton Inference Server.

The PIDC sought to reduce property damage and address public safety concerns about crashes and speeding. When average speeds are higher than what is recommended on certain road segments, traffic calming measures are being implemented using speed insights.

NVIDIA Jetson Xavier NX to Monitor Pollution in Real Time

Rekor’s vehicle identification models, which were powered by NVIDIA Jetson Xavier NX modules, were able to follow pollution to its origins, moving it one step closer to mitigation than the conventional method of using satellite data to attempt to comprehend its placements.

In the future, Rekor is investigating the potential applications of NVIDIA Omniverse for the creation of digital twins to model traffic reduction using various techniques. Omniverse is a platform for creating OpenUSD applications for generative physical AI and industrial digitization.

Creating digital twins for towns using Omniverse has significant ramifications for lowering traffic, pollution, and traffic fatalities all of which Rekor views as being very advantageous for its clients.

Read more on Govindhtech.com

[track-maniac]

sentences that should be illegal to say to a girl:

This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations

TF-TRT Warning: Could not find TensorRT

Cannot dlopen some GPU libraries

[gadgetsboy]

MediaTek and NVIDIA Team up for Automotive AI

With more and more auto manufacturers pushing for smarter vehicles, there's been a considerably growing demand for more powerful smart automotive platforms, going beyond the simple act of pairing your smartphone with your car's Bluetooth console (think 'K.I.T.T.' from Knight Rider). It's no surprise then that we've seen an uptick of specially-designed hardware and software solutions that provide entertainment and navigation features for drivers and passengers alike. With that being said, MediaTek's push towards putting more AI tech into everyday consumer products has certainly yielded some very interesting results, and the company's newly-announced collaboration with PC gaming giant NVIDIA aims to do the same, at least in terms of automotive applications. More specifically, the mobile chip manufacturer formally announced that it has entered into a partnership with NVIDIA to develop new AI-powered software for vehicles, with the goal of creating a "smart cabin" for drivers and passengers. This collaboration will enable MediaTek to develop automotive SoCs, which will in turn integrate a new NVIDIA GPU "chiplet" with support for NVIDIA AI and graphics IP. Interestingly, these chiplets will be connected by specially-developed interconnect technology, at least according to MediaTek. Rick Tsai, Vice Chairman and CEO of MediaTek states: “NVIDIA is a world-renowned pioneer and industry leader in AI and computing. With this partnership,our collaborative vision is to provide a global one-stop shop for the automotive industry, designing thenext generation of intelligent, always-connected vehicles. Through this special collaboration with NVIDIA, we will together be able to offer a truly unique platform for the compute intensive, software-defined vehicle of the future.” NVIDIA CEO Jensen Huang says this combination of MediaTek and NVIDIA hardware will "enable new user experiences, enhanced safety and new connected services for all vehicle segments, from luxury to mainstream.” MediaTek adds that its smart cabin solutions will run NVIDIA DRIVE OS, DRIVE IX, CUDA and TensorRT software technologies. This then allows consumers to experience a full range of AI cabin and cockpit functionality with integrated AI, safety, and security features as well. While NVIDIA is more known to consumers as a PC and gaming-centric brand, the company does put a considerable amount of investment towards the development and production of AI and IoT (internet of things) technology, in addition to its powerful GPUs and processors. The Taiwanese company further states that by allowing MediaTek to tap into NVIDIA’s core expertise in AI, cloud, graphics technology, software and pairing with NVIDIA ADAS solutions, we can expect to see further improvement to the capabilities of the Dimensity Auto platform, MediaTek's flagship automotive software product. Dimensity Auto is designed for vehicles with support for compatible smart features. With all that being said, it should be interesting to see how both companies approach this new partnership, both on hardware and business fronts. Read the full article

[3acesnews]

NVIDIA's TensorRT-LLM MultiShot Enhances AllReduce Performance with NVSwitch

[avocodedigital]

Nvidia Open-Source LLM - GPT-4 Rival

Join the newsletter: https://avocode.digital/newsletter/

Introduction to Nvidia's Open-Source LLM

The tech world is abuzz with excitement as Nvidia, a leader in computing power and graphics processing, has officially released its open-source Large Language Model (LLM), which many are calling a rival to OpenAI's famed GPT-4. This strategic move marks Nvidia's deeper foray into the realm of artificial intelligence, positioning itself as a formidable competitor in the AI landscape. With advancements that suggest it might be on par with, or even surpass, current industry standards, this innovation has captivated both developers and tech enthusiasts alike.

Why Nvidia's Move Matters

Nvidia's decision to introduce an open-source LLM is significant for several reasons: 1. Democratization of AI technology: By releasing this model as open-source, Nvidia is enabling developers, researchers, and organizations across the globe to access cutting-edge AI technology. This accessibility fosters innovation and collaboration across various sectors such as healthcare, finance, and entertainment. 2. Competition Drives Innovation: With GPT-4 setting a high standard, Nvidia's entry into the space shows healthy competition. This rivalry pushes both companies to continuously improve and innovate, benefiting the entire tech ecosystem. 3. Leverage of Computational Power: Nvidia is renowned for its high-performance GPUs. By integrating its LLM with its hardware, it promises unparalleled performance and efficiency, setting a new benchmark in AI processing power.

Nvidia's LLM Features and Capabilities

Nvidia's open-source LLM brings several innovative features to the table:

Advanced Natural Language Processing

The model boasts highly sophisticated NLP abilities, capable of understanding and generating human-like text. Its prowess in language comprehension and generation makes it ideal for applications ranging from chatbots to complex data analysis.

Enhanced Scalability

Built to be scalable, Nvidia's model can be deployed across various platforms, from personal computers to large data centers. This flexibility ensures that businesses of all sizes can leverage its capabilities without sacrificing performance or incurring excessive costs.

Integration with Nvidia's Ecosystem

The open-source LLM seamlessly integrates with Nvidia's existing ecosystem. Developers can take advantage of Nvidia's CUDA and TensorRT for efficient deployment, while the model benefits from the acceleration provided by Nvidia GPUs. This symbiosis results in faster training times and real-time AI applications.

Comparing Nvidia's LLM with GPT-4

While Nvidia's open-source endeavor invites comparisons to OpenAI's GPT-4, there are distinct differences that merit attention: -

Open-Source Approach: Unlike GPT-4, which is proprietary, Nvidia's LLM is open-source, encouraging innovation and adaptation across diverse user groups.

-

Hardware Optimization: Nvidia's model is optimized for its GPU architecture, providing potentially superior performance metrics in some scenarios compared to GPT-4.

-

Community Involvement: By allowing a broader range of contributions and experiments from the tech community, Nvidia’s model could evolve rapidly in ways that GPT-4 may not.

Potential Applications

The possibilities with Nvidia's LLM are endless, spanning multiple industries and applications:

Healthcare

In healthcare, the LLM can be utilized for accurate diagnostic predictions by analyzing patient data and medical literature to provide insights and potential treatment plans.

Automated Customer Service

Businesses can customize the LLM to develop intelligent chatbots and virtual assistants that offer personalized customer interactions, enhancing user satisfaction and operational efficiency.

Content Creation

The model's sophisticated language generation capabilities can aid media companies by streamlining content creation processes, aiding in the production of articles, scripts, or even creative writing projects.

Challenges and Considerations

While the potential benefits of Nvidia's open-source LLM are substantial, there are challenges and considerations to address:

Data Privacy and Security

With AI models handling sensitive data, ensuring strict adherence to data privacy laws and using secure data handling practices is crucial.

Ethical Concerns

Like other AI models, Nvidia's LLM must contend with ethical concerns such as bias and misinformation. Developers need to actively work towards minimizing biases in training data and ensuring the responsible use of AI technology.

The Future of AI with Nvidia's Open-Source LLM

As Nvidia steps forward with its LLM, the future of AI appears increasingly dynamic and collaborative. The open-source model not only levels the playing field by providing access to advanced AI technology but also motivates other tech giants to innovate at a similar pace. In conclusion, Nvidia's introduction of its open-source LLM signifies a pivotal moment in the AI industry. By making sophisticated AI accessible and encouraging a collaborative spirit, Nvidia is not only aiming for parity with GPT-4 but also charting a new course for AI development, one marked by openness and innovation. This development represents a quantum leap forward in how LLMs can be built, shared, and utilized across industries, setting the stage for an exciting future in artificial intelligence. Want more? Join the newsletter: https://avocode.digital/newsletter/

[jcmarchi]

Some Non-Obvious Points About OpenAI 01

New Post has been published on https://thedigitalinsider.com/some-non-obvious-points-about-openai-01/

Some Non-Obvious Points About OpenAI 01

Plus some major funding rounds by World Labs and Glean , Mistral’s new release and more.

Image Credit: OpenAI

Next Week in The Sequence:

Edge 431: Our series about space state models(SSMs) continues with an overview of multimodal SSMs. We discuss the Cobra SSM multimodal model and NVIDIA’s TensorRT-LLM framework.

Edge 432: Dives into NVIDIA’s Minitron models distilled from Llama 3.1.

You can subscribe to The Sequence below:

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

📝 Editorial: Some Non-Obvious Points About OpenAI 01

The release of OpenAI’s new model dominated headlines this week. The o1 models are specialized in reasoning and planning, areas that have long been of interest to OpenAI. Much of the debate in online circles has focused on the model’s specific capabilities, such as whether the terms “reasoning” and “thinking” are appropriate, so there is plenty of content discussing that. Instead of contributing to the debate, I wanted to highlight a few key points that I found particularly interesting while reading the o1 technical report.

It seems that the o1 models were trained and fine-tuned using different methodologies compared to their predecessors. Specifically, OpenAI used reinforcement learning optimized for chain of thought (CoT) scenarios, which is somewhat unique.

Initial results indicate that this reinforcement learning for CoT technique can scale significantly, potentially leading to new breakthroughs in reasoning and planning.

Only CoT summaries, rather than complete CoT traces, are available via the API, making it difficult to determine how the model arrives at specific outputs.

Somewhat paradoxically, CoT-focused models might lower the entry point for interpretability since we are starting with a baseline of reasoning traces.

One of the most interesting aspects of o1 is the shift from training to inference compute time. Inference, rather than training, is increasingly becoming a key requirement for complex reasoning tasks. The reasoning core doesn’t necessarily need to be a large model, which could translate into decreases in training time. We will need to see how this strategy evolves over time.

This point makes me think we might be witnessing the start of a new set of scaling laws focused on inference.

The red-teaming efforts for o1, with companies such as Apollo Research and Haize Labs, are quite impressive and worth diving into in the technical report.

Unsurprisingly, o1 is much harder to jailbreak than previous models, and it spends much more time on inference. That said, there have already been several successful jailbreak attempts.

OpenAI o1 clearly shows that reasoning is one of the next frontiers of foundation model research and, more importantly, that improvements in foundation model architectures are not stalling—they may just take some time to materialize.

🔎 ML Research

LLMs for Novel Research Ideas

AI researchers from Stanford University published a study about the research ideation capabilities of LLMs. The experiment draws a comparison between human- and LLM generated ideas across different nove fields. The results might surprise you —> Read more.

Agent Workflow Memory

Researchers from MIT and Carnegie Mellon University published a paper introducing Agent Workflow Memory(AWM), a method for reusable tasks workflows in agents. AWM, introduces reusable tasks to agents so that they can be used to guide future actions —> Read more.

Modular LLMs

Researchers from Princeton University, Carnegie Mellon University , Tsinghua University, UCLA and several other AI labs published a paper proposing a modular design for LLMs. Specifically, the paper introduces the term of “brick” to define a functional block within an LLM and highlights the efficiencies of following this composable approch for LLM construction —> Read more.

Better Math Agents

Google DeepMind published a paper introducing a preference learning framework to optimize the performance of math AI models. The framework uses techniques such as multi-turn and tool-integrated reasoning to improve the efficiency of single-turn math models —> Read more.

WINDOWSAGENTARENA

Researchers from Microsoft, Columbia University and Carnegie Mellon University published a paper detailing WINDOWSAGENTARENA, an environment for evaluating agents in tasks in the Windows OS. The environment includes over 150 diverse tasks that requires capabilites such as screen understanding, tool usage and planning —> Read more.

LLaMA-Omni

Researchers from several elite chinese AI labs published a paper proposing LLaMA-Omni, an architecture for integrating speech interactions with open source LLMs. LLaMA-Omni integrates a pretrained speech encoder, a speech adapter and a streaming speech decoder with an LLM such as LLaMA in order to process text and speech data simulataneously —> Read more.

🤖 AI Tech Releases

OpenAI o1

OpenAI released a new family of models specialized in reasoning —> Read more.

AgentForce

Salesforce unveiled AgentForce, its platform for autonomous AI agents —> Read more.

DataGemma

Google open sourced DataGemma, a series of small models grounded in factual data —> Read more.

Pixtral 12B

Mistral released Pixtral 12B, its first multimodal model for images and text —> Read more.

🛠 Real World AI

AI for Coding at Salesforce

Salesforce discusses CodeGenie, an internal tool used to boost developer productivity using generative AI —> Read more.

Data Center Cooling at Meta

Meta discusses the reinforcement learning techniques used for cooling optimization in their data centers —> Read more.

📡AI Radar

AI pioneer Fei-Fei Li’s company World Labs raised another $230 million.

AI-search platform Glean raised $260 million in a Series E.

OpenAI is rumoured to be raising a new round at a $150 billion valuation.

Google co-founder Sergey Brin gave a rare interview about his recent work on AI.

Arcee AI released its SuperNova 70B model.

AI agent platform Landbase came out of stealth with $12.5 million in funding.

InMobi secured $100 million for AI acquisition ahead of its IPO.

AI bookkeeping startup Finally raised $200 million.

Stability AI and Lenovo partnered for text-to-image capabilities.

AI translation platform Smartcat raised $43 million.

ServiceNow unveiled a series of AI agents for customer service, procurement, HR and others.

OffDeal announced a $4.7 million round to improve M&A for small businesses.

AI-powered compliance platform Datricks raised $15 million in a new round.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

[vastperhaps]

Whisper with TensorRT-LLM - Baseten

[secourses]

Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI

Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI : https://youtu.be/HKX8_F1Er_w

Do not skip any part of this tutorial to master how to use Stable Diffusion 3 (SD3) with the most advanced generative AI open source APP SwarmUI. Automatic1111 SD Web UI or Fooocus are not supporting the #SD3 yet. Therefore, I am starting to make tutorials for SwarmUI as well. #StableSwarmUI is officially developed by the StabilityAI and your mind will be blown after you watch this tutorial and learn its amazing features. StableSwarmUI uses #ComfyUI as the back end thus it has all the good features of ComfyUI and it brings you easy to use features of Automatic1111 #StableDiffusion Web UI with them. I really liked SwarmUI and planning to do more tutorials for it.

🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985

0:00 Introduction to the Stable Diffusion 3 (SD3) and SwarmUI and what is in the tutorial 4:12 Architecture and features of SD3 5:05 What each different model files of Stable Diffusion 3 means 6:26 How to download and install SwarmUI on Windows for SD3 and all other Stable Diffusion models 8:42 What kind of folder path you should use when installing SwarmUI 10:28 If you get installation error how to notice and fix it 11:49 Installation has been completed and now how to start using SwarmUI 12:29 Which settings I change before start using SwarmUI and how to change your theme like dark, white, gray 12:56 How to make SwarmUI save generated images as PNG 13:08 How to find description of each settings and configuration 13:28 How to download SD3 model and start using on Windows 13:38 How to use model downloader utility of SwarmUI 14:17 How to set models folder paths and link your existing models folders in SwarmUI 14:35 Explanation of Root folder path in SwarmUI 14:52 VAE of SD3 do we need to download? 15:25 Generate and model section of the SwarmUI to generate images and how to select your base model 16:02 Setting up parameters and what they do to generate images 17:06 Which sampling method is best for SD3 17:22 Information about SD3 text encoders and their comparison 18:14 First time generating an image with SD3 19:36 How to regenerate same image 20:17 How to see image generation speed and step speed and more information 20:29 Stable Diffusion 3 it per second speed on RTX 3090 TI 20:39 How to see VRAM usage on Windows 10 22:08 And testing and comparing different text encoders for SD3 22:36 How to use FP16 version of T5 XXL text encoder instead of default FP8 version 25:27 The image generation speed when using best config for SD3 26:37 Why VAE of the SD3 is many times better than previous Stable Diffusion models, 4 vs 8 vs 16 vs 32 channels VAE 27:40 How to and where to download best AI upscaler models 29:10 How to use refiner and upscaler models to improve and upscale generated images 29:21 How to restart and start SwarmUI 32:01 The folders where the generated images are saved 32:13 Image history feature of SwarmUI 33:10 Upscaled image comparison 34:01 How to download all upscaler models at once 34:34 Presets feature in depth 36:55 How to generate forever / infinite times

37:13 Non-tiled upscale caused issues 38:36 How to compare tiled vs non-tiled upscale and decide best 39:05 275 SwarmUI presets (cloned from Fooocus) I prepared and the scripts I coded to prepare them and how to import those presets 42:10 Model browser feature 43:25 How to generate TensorRT engine for huge speed up 43:47 How to update SwarmUI 44:27 Prompt syntax and advanced features 45:35 How to use Wildcards (random prompts) feature 46:47 How to see full details / metadata of generated images 47:13 Full guide for extremely powerful grid image generation (like X/Y/Z plot) 47:35 How to put all downloaded upscalers from zip file 51:37 How to see what is happening at the server logs 53:04 How to continue grid generation process after interruption 54:32 How to open grid generation after it has been completed and how to use it 56:13 Example of tiled upscaling seaming problem

1:00:30 Full guide for image history 1:02:22 How to directly delete images and star them 1:03:20 How to use SD 1.5 and SDXL models and LoRAs 1:06:24 Which sampler method is best 1:06:43 How to use image to image 1:08:43 How to use edit image / inpainting 1:10:38 How to use amazing segmentation feature to automatically inpaint any part of images 1:15:55 How to use segmentation on existing images for inpainting and get perfect results with different seeds 1:18:19 More detailed information regarding upscaling and tiling and SD3 1:20:08 Seams perfect explanation and example and how to fix it 1:21:09 How to use queue system 1:21:23 How to use multiple GPUs with adding more backends 1:24:38 Loading model in low VRAM mode 1:25:10 How to fix colors over saturation 1:27:00 Best image generation configuration for SD3 1:27:44 How to apply upscale to your older generated images quickly via preset 1:28:39 Other amazing features of SwarmUI 1:28:49 Clip tokenization and rare token OHWX

[govindhtech]

NVIDIA Nemotron-4 340B Open LLMs for Synthetic Data Training

NVIDIA Nemotron-4 340B

NVIDIA unveiled Nemotron-4 340B, an open model family that allows developers to produce synthetic data for large language model (LLM) training in the industrial, retail, healthcare, and finance sectors, among other industries.

Robust training datasets might be prohibitively expensive and difficult to get, but they are essential to the performance, accuracy, and quality of responses from a bespoke LLM.

Nemotron-4 340B provides developers with a scalable, free method of creating synthetic data that may be used to construct robust LLMs, with a uniquely liberal open model licence.

Nemotron

The base, instruct, and reward models in the Nemotron-4 340B family work together to create synthetic data that is used to train and improve LLMs. The models are designed to function with NVIDIA NeMo, an open-source platform that enables data curation, customisation, and evaluation during the whole model training process. Additionally, they are designed using the open-source NVIDIA TensorRT-LLM library in mind for inference.

You may now get Nemotron-4 340B from Hugging Face. The models will be packaged as an NVIDIA NIM microservice with a standard application programming interface that can be deployed anywhere.

Getting Around the Nemotron to Produce Synthetic Data

LLMs can be useful in situations where access to big, diverse labelled datasets is limited for developers creating synthetic training data.

The Nemotron-4 340B Instruct model generates a variety of synthetic data that closely resembles real-world data, enhancing data quality to boost the robustness and performance of custom LLMs in a range of domains.

A large language model (LLM) called Nemotron-4-340B-Instruct can be utilised in a pipeline for synthetic data creation to produce training data that will aid in the development of LLMs by researchers and developers. This is a refined Nemotron-4-340B-Base model designed for English-speaking single- and multi-turn chat scenarios. A context length of 4,096 tokens is supported.

A dataset of 9 trillion tokens, comprising a wide range of English-based literature, more than 50 natural languages, and more than 40 coding languages, was used to pre-train the base model. The Nemotron-4-340B-Instruct model then underwent more alignment procedures, such as:

Monitoring and Adjustment (SFT)

Optimisation of Direct Preference (DPO)

Preference Optimisation with Reward Awareness (RPO)

While over 98% of the data utilised for supervised fine-tuning and preference fine-tuning (DPO & RPO) was synthesised by NVIDIA’s data creation pipeline, the company only relied on about 20,000 human-annotated data throughout the alignment process.

As a result, a model that can produce high-quality synthetic data for a range of use scenarios is created that is matched for human chat preferences and enhances mathematical thinking, coding, and instruction following.

NVIDIA affirms under the terms of the NVIDIA Open Model Licence:

The models can be used commercially.

It is not prohibited for you to develop and share derivative models.

Any outputs produced utilising the Models or Derivative Models are not attributed to NVIDIA.

Developers can then utilise the Nemotron-4 340B Reward model to filter for high-quality responses, which will improve the quality of the AI-generated data. Five criteria are used by Nemotron-4 340B Reward to score responses: verbosity, coherence, accuracy, helpfulness, and complexity. As of right now, it holds the top spot on the AI2-created Hugging Face RewardBench scoreboard, which assesses the strengths, vulnerabilities, and safety of reward models.

By combining their private data with the included HelpSteer2 dataset, researchers can further customise the Nemotron-4 340B Base model to construct their own teach or reward models.

Large language models (LLMs) such as Nemotron-4-340B-Base can be utilised in a synthetic data production pipeline to produce training data that aids in the development of LLMs by researchers and developers. With 4,096 tokens in the context, this model supports 340 billion parameters. It has been pre-trained on a total of 9 trillion tokens, which include more than 40 coding languages, more than 50 natural languages, and a wide range of English-based writings.

To enhance the quality of the pre-trained model, a continuous pre-training of 1 trillion tokens was carried out on top of the pre-trained model following an initial pre-training phase of 8 trillion tokens. NVIDIA changed the distribution of the data used during continuous pre-training from the one that was present at the start of training.

TensorRT-LLM Inference Optimisation, NeMo Fine-Tuning

Developers can maximise the effectiveness of their instruct and reward models to provide synthetic data and score responses by utilising the open-source NVIDIA NeMo and NVIDIA TensorRT-LLM.

Tensor parallelism a kind of model parallelism in which individual weight matrices are divided among several GPUs and servers is a sort of parallelism that is optimised into all Nemotron-4 340B models using TensorRT-LLM. This allows for effective inference at scale.

Nemotron-4 340B the NeMo architecture allows Base, which was trained on 9 trillion tokens, to be tailored to certain use cases or domains. Extensive pretraining data aids in this fine-tuning process, which produces outputs that are more accurate for particular downstream tasks.

The NeMo framework offers a range of customisation options, such as parameter-efficient fine-tuning techniques like low-rank adaptation, or LoRA, and supervised fine-tuning techniques.

Developers can use NeMo Aligner and datasets annotated by Nemotron-4 340B Reward to align their models and improve model quality. Using methods like reinforcement learning from human feedback (RLHF), a model’s behaviour is refined during alignment, a crucial phase in LLM training, to make sure its outputs are accurate, safe, acceptable for the context, and compatible with the model’s stated goals.

NeMo and TensorRT-LLM are also available to businesses via the cloud-native NVIDIA AI Enterprise software platform, which offers rapid and effective runtimes for generative AI foundation models. This platform is ideal for those looking for enterprise-grade support and security for production environments.

Assessing Model Security and Beginning

After undergoing a thorough safety examination that included adversarial tests, the Nemotron-4 340B Instruct model demonstrated good performance over a broad spectrum of risk indicators. It is still important for users to carefully assess the model’s outputs to make sure the artificially created data is appropriate, secure, and accurate for their use case.

Read more on Govindhtech.com

[newspatron]

Chat with RTX: Create Your Own AI Chatbot

We hope you enjoyed this article about Chat with RTX, NVIDIA and generative AI. Please share your feedback, questions, or comments below. We would love to hear from you and learn from your experience.

Image Source – Newspatron Creative Team AI-Generated Image for representative purpose [Read About Us to know more] Do you want to have your own personal assistant, tutor, or friend that can answer any question you have, help you with any task you need, or entertain you with any topic you like? If yes, then you should check out Chat with RTX, a free tech demo from NVIDIA that lets you create…

[exeton]

Supercharging Generative AI: The Power of NVIDIA RTX AI PCs and Cloud Workstations

Introduction

Generative AI is revolutionizing the world of Windows applications and gaming. It’s enabling dynamic NPCs, helping creators generate new art, and boosting gamers’ frame rates by up to 4x. But this is just the beginning. As the capabilities and use cases for generative AI grow, so does the demand for robust compute resources. Enter NVIDIA RTX AI PCs and workstations that tap into the cloud to supercharge these AI-driven experiences. Let’s dive into how hybrid AI solutions combine local and cloud-based computing to meet the evolving demands of AI workloads.

Hybrid AI: A Match Made in Tech Heaven

As AI adoption continues to rise, developers need versatile deployment options. Running AI locally on NVIDIA RTX GPUs offers high performance, low latency, and constant availability, even without internet connectivity. On the other hand, cloud-based AI can handle larger models and scale across multiple GPUs, serving many clients simultaneously. Often, a single application will leverage both approaches.

Hybrid AI harmonizes local PC and workstation compute power with cloud scalability, providing the flexibility to optimize AI workloads based on specific use cases, cost, and performance. This setup ensures that AI tasks run efficiently, whether they are local or cloud-based, all accelerated by NVIDIA GPUs and the comprehensive NVIDIA AI stack, including TensorRT and TensorRT-LLM.

Tools and Technologies Supporting Hybrid AI

NVIDIA offers a range of tools and technologies to support hybrid AI workflows for creators, gamers, and developers. Let’s explore how these innovations are transforming various industries.

Dream in the Cloud, Create Locally on RTX

Generative AI is a game-changer for artists, enabling them to ideate, prototype, and brainstorm new creations. One such solution, Generative AI by iStock — powered by NVIDIA Edify — provides a generative photography service built for artists. It trains on licensed content and compensates contributing artists.

Generative AI by iStock offers tools for exploring styles, modifying parts of an image, and expanding the canvas, allowing artists to quickly bring their ideas to life. Once the creative concept is ready, artists can switch to their local RTX-powered PCs and workstations. These systems provide AI acceleration in over 125 top creative apps, allowing artists to realize their full vision, whether they are using Photoshop, DaVinci Resolve, or Blender.

Bringing NPCs to Life with Hybrid ACE

Hybrid AI is also revolutionizing interactive PC gaming. NVIDIA ACE enables game developers to integrate state-of-the-art generative AI models into digital avatars on RTX AI PCs. Powered by AI neural networks, NVIDIA ACE allows developers to create NPCs that understand and respond to human player text and speech in real-time, enhancing the gaming experience.

Hybrid Developer Tools for Versatile AI Model Building

Hybrid AI also facilitates the development and fine-tuning of new AI models. NVIDIA AI Workbench allows developers to quickly create, test, and customize pretrained generative AI models and LLMs on RTX GPUs. With streamlined access to popular repositories like Hugging Face, GitHub, and NVIDIA NGC, AI Workbench simplifies the development process, enabling data scientists and developers to collaborate and migrate projects seamlessly.

When additional performance is needed, projects can scale to data centers, public clouds, or NVIDIA DGX Cloud. They can then be brought back to local RTX systems for inference and light customization. Pre-built Workbench projects support tasks such as document chat using retrieval-augmented generation (RAG) and customizing LLMs using fine-tuning.

The Hybrid RAG Workbench Project

The Hybrid RAG Workbench project provides a customizable application that developers can run locally or in the cloud. It allows developers to embed documents locally and run inference either on a local RTX system or a cloud endpoint hosted on NVIDIA’s API catalog. This flexibility supports various models, endpoints, and containers, ensuring developers can optimize performance based on their GPU of choice.

Conclusion

NVIDIA RTX AI PCs and workstations, combined with cloud-based solutions, offer a powerful platform for creators, gamers, and developers. By leveraging hybrid AI workflows, users can take advantage of the best of both worlds, achieving high performance, scalability, and flexibility in their AI-driven projects.

Generative AI is transforming gaming, videoconferencing, and interactive experiences of all kinds. Stay informed about the latest developments and innovations by subscribing to the AI Decoded newsletter. And if you found this article helpful, consider supporting us! Your support can make a significant difference in our progress and innovation!

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

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Did we learn nothing from mad cow syndrome

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Enhancing Large Language Models with NVIDIA Triton and TensorRT-LLM on Kubernetes

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Nemotron-4 340B: Open Models for Synthetic Data Generation

NVIDIA has recently unveiled a groundbreaking family of open models called Nemotron-4 340B, designed specifically for generating synthetic data to train large language models (LLMs) across various industries. This innovative development promises to revolutionize the way we approach LLM training and unlock new possibilities in diverse domains.

The Nemotron-4 340B models offer a powerful solution to one of the most significant challenges in the field of natural language processing (NLP) – the scarcity of high-quality training data. By leveraging these open models, researchers and developers can generate synthetic data at an unprecedented scale, enabling more efficient and effective training of LLMs for a wide range of applications.

Key Features:

The Nemotron-4 340B family comprises several model variants, each tailored to specific use cases:

Nemotron-4-340B-Base: The foundational model, serving as the backbone for synthetic data generation.

Nemotron-4-340B-Instruct: A fine-tuned variant optimized for English-based chat and conversational use cases.

Nemotron-4-340B-Reward: Another specialized variant within the family, designed for specific tasks.

One of the most compelling aspects of the Nemotron-4 340B models is their accessibility. NVIDIA has made these models available under the NVIDIA Open Model License Agreement, allowing for free use for both research and commercial purposes. This open approach fosters collaboration, innovation, and accelerates the development of advanced NLP applications.

Performance and Evaluation

The Nemotron-4 340B models have demonstrated competitive performance on various evaluation benchmarks, showcasing their efficacy in generating high-quality synthetic data. Remarkably, over 98% of the model alignment data used during training was synthetically generated, highlighting the potential of these models to overcome data scarcity challenges.

Deployment and Scalability

Designed with scalability in mind, the Nemotron-4 340B models are sized to fit on a single DGX H100 system with 8 GPUs, enabling efficient deployment and utilization of resources. This scalability ensures that these models can be leveraged by a wide range of organizations, from academic institutions to large enterprises.

Synthetic Data Pipeline

In addition to the models themselves, NVIDIA has open-sourced the synthetic data generation pipeline used during model alignment. This transparency not only promotes reproducibility but also empowers researchers and developers to understand and potentially extend or modify the pipeline to suit their specific needs.

The introduction of the Nemotron-4 340B models represents a significant milestone in the field of NLP and synthetic data generation. By providing open access to these powerful models, NVIDIA is fostering a collaborative ecosystem where researchers, developers, and organizations can collectively push the boundaries of natural language understanding and AI applications. As the demand for LLMs continues to grow across various industries, the Nemotron-4 340B models offer a promising solution to the data challenges that have traditionally hindered progress in this domain.

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TensorRT-LLM: A Comprehensive Guide to Optimizing Large Language Model Inference for Maximum Performance

New Post has been published on https://thedigitalinsider.com/tensorrt-llm-a-comprehensive-guide-to-optimizing-large-language-model-inference-for-maximum-performance/

TensorRT-LLM: A Comprehensive Guide to Optimizing Large Language Model Inference for Maximum Performance

As the demand for large language models (LLMs) continues to rise, ensuring fast, efficient, and scalable inference has become more crucial than ever. NVIDIA’s TensorRT-LLM steps in to address this challenge by providing a set of powerful tools and optimizations specifically designed for LLM inference. TensorRT-LLM offers an impressive array of performance improvements, such as quantization, kernel fusion, in-flight batching, and multi-GPU support. These advancements make it possible to achieve inference speeds up to 8x faster than traditional CPU-based methods, transforming the way we deploy LLMs in production.

This comprehensive guide will explore all aspects of TensorRT-LLM, from its architecture and key features to practical examples for deploying models. Whether you’re an AI engineer, software developer, or researcher, this guide will give you the knowledge to leverage TensorRT-LLM for optimizing LLM inference on NVIDIA GPUs.

Speeding Up LLM Inference with TensorRT-LLM

TensorRT-LLM delivers dramatic improvements in LLM inference performance. According to NVIDIA’s tests, applications based on TensorRT show up to 8x faster inference speeds compared to CPU-only platforms. This is a crucial advancement in real-time applications such as chatbots, recommendation systems, and autonomous systems that require quick responses.

How It Works

TensorRT-LLM speeds up inference by optimizing neural networks during deployment using techniques like:

Quantization: Reduces the precision of weights and activations, shrinking model size and improving inference speed.

Layer and Tensor Fusion: Merges operations like activation functions and matrix multiplications into a single operation.

Kernel Tuning: Selects optimal CUDA kernels for GPU computation, reducing execution time.

These optimizations ensure that your LLM models perform efficiently across a wide range of deployment platforms—from hyperscale data centers to embedded systems.

Optimizing Inference Performance with TensorRT

Built on NVIDIA’s CUDA parallel programming model, TensorRT provides highly specialized optimizations for inference on NVIDIA GPUs. By streamlining processes like quantization, kernel tuning, and fusion of tensor operations, TensorRT ensures that LLMs can run with minimal latency.

Some of the most effective techniques include:

Quantization: This reduces the numerical precision of model parameters while maintaining high accuracy, effectively speeding up inference.

Tensor Fusion: By fusing multiple operations into a single CUDA kernel, TensorRT minimizes memory overhead and increases throughput.

Kernel Auto-tuning: TensorRT automatically selects the best kernel for each operation, optimizing inference for a given GPU.

These techniques allow TensorRT-LLM to optimize inference performance for deep learning tasks such as natural language processing, recommendation engines, and real-time video analytics.

Accelerating AI Workloads with TensorRT

TensorRT accelerates deep learning workloads by incorporating precision optimizations such as INT8 and FP16. These reduced-precision formats allow for significantly faster inference while maintaining accuracy. This is particularly valuable in real-time applications where low latency is a critical requirement.

INT8 and FP16 optimizations are particularly effective in:

Video Streaming: AI-based video processing tasks, like object detection, benefit from these optimizations by reducing the time taken to process frames.

Recommendation Systems: By accelerating inference for models that process large amounts of user data, TensorRT enables real-time personalization at scale.

Natural Language Processing (NLP): TensorRT improves the speed of NLP tasks like text generation, translation, and summarization, making them suitable for real-time applications.

Deploy, Run, and Scale with NVIDIA Triton

Once your model has been optimized with TensorRT-LLM, you can easily deploy, run, and scale it using NVIDIA Triton Inference Server. Triton is an open-source software that supports dynamic batching, model ensembles, and high throughput. It provides a flexible environment for managing AI models at scale.

Some of the key features include:

Concurrent Model Execution: Run multiple models simultaneously, maximizing GPU utilization.

Dynamic Batching: Combines multiple inference requests into one batch, reducing latency and increasing throughput.

Streaming Audio/Video Inputs: Supports input streams in real-time applications, such as live video analytics or speech-to-text services.

This makes Triton a valuable tool for deploying TensorRT-LLM optimized models in production environments, ensuring high scalability and efficiency.

Core Features of TensorRT-LLM for LLM Inference

Open Source Python API

TensorRT-LLM provides a highly modular and open-source Python API, simplifying the process of defining, optimizing, and executing LLMs. The API enables developers to create custom LLMs or modify pre-built ones to suit their needs, without requiring in-depth knowledge of CUDA or deep learning frameworks.

In-Flight Batching and Paged Attention

One of the standout features of TensorRT-LLM is In-Flight Batching, which optimizes text generation by processing multiple requests concurrently. This feature minimizes waiting time and improves GPU utilization by dynamically batching sequences.

Additionally, Paged Attention ensures that memory usage remains low even when processing long input sequences. Instead of allocating contiguous memory for all tokens, paged attention breaks memory into “pages” that can be reused dynamically, preventing memory fragmentation and improving efficiency.

Multi-GPU and Multi-Node Inference

For larger models or more complex workloads, TensorRT-LLM supports multi-GPU and multi-node inference. This capability allows for the distribution of model computations across several GPUs or nodes, improving throughput and reducing overall inference time.

FP8 Support

With the advent of FP8 (8-bit floating point), TensorRT-LLM leverages NVIDIA’s H100 GPUs to convert model weights into this format for optimized inference. FP8 enables reduced memory consumption and faster computation, especially useful in large-scale deployments.

TensorRT-LLM Architecture and Components

Understanding the architecture of TensorRT-LLM will help you better utilize its capabilities for LLM inference. Let’s break down the key components:

Model Definition

TensorRT-LLM allows you to define LLMs using a simple Python API. The API constructs a graph representation of the model, making it easier to manage the complex layers involved in LLM architectures like GPT or BERT.

Weight Bindings

Before compiling the model, the weights (or parameters) must be bound to the network. This step ensures that the weights are embedded within the TensorRT engine, allowing for fast and efficient inference. TensorRT-LLM also allows for weight updates after compilation, adding flexibility for models that need frequent updates.

Pattern Matching and Fusion

Operation Fusion is another powerful feature of TensorRT-LLM. By fusing multiple operations (e.g., matrix multiplications with activation functions) into a single CUDA kernel, TensorRT minimizes the overhead associated with multiple kernel launches. This reduces memory transfers and speeds up inference.

Plugins

To extend TensorRT’s capabilities, developers can write plugins—custom kernels that perform specific tasks like optimizing multi-head attention blocks. For instance, the Flash-Attention plugin significantly improves the performance of LLM attention layers.

Benchmarks: TensorRT-LLM Performance Gains

TensorRT-LLM demonstrates significant performance gains for LLM inference across various GPUs. Here’s a comparison of inference speed (measured in tokens per second) using TensorRT-LLM across different NVIDIA GPUs:

Model Precision Input/Output Length H100 (80GB) A100 (80GB) L40S FP8 GPTJ 6B FP8 128/128 34,955 11,206 6,998 GPTJ 6B FP8 2048/128 2,800 1,354 747 LLaMA v2 7B FP8 128/128 16,985 10,725 6,121 LLaMA v3 8B FP8 128/128 16,708 12,085 8,273

These benchmarks show that TensorRT-LLM delivers substantial improvements in performance, particularly for longer sequences.

Hands-On: Installing and Building TensorRT-LLM

Step 1: Create a Container Environment

For ease of use, TensorRT-LLM provides Docker images to create a controlled environment for building and running models.

docker build --pull --target devel --file docker/Dockerfile.multi --tag tensorrt_llm/devel:latest .

Step 2: Run the Container

Run the development container with access to NVIDIA GPUs:

docker run --rm -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --gpus=all --volume $PWD:/code/tensorrt_llm --workdir /code/tensorrt_llm tensorrt_llm/devel:latest

Step 3: Build TensorRT-LLM from Source

Inside the container, compile TensorRT-LLM with the following command:

python3 ./scripts/build_wheel.py --trt_root /usr/local/tensorrt pip install ./build/tensorrt_llm*.whl

This option is particularly useful when you want to avoid compatibility issues related to Python dependencies or when focusing on C++ integration in production systems. Once the build completes, you will find the compiled libraries for the C++ runtime in the cpp/build/tensorrt_llm directory, ready for integration with your C++ applications.

Step 4: Link the TensorRT-LLM C++ Runtime

When integrating TensorRT-LLM into your C++ projects, ensure that your project’s include paths point to the cpp/include directory. This contains the stable, supported API headers. The TensorRT-LLM libraries are linked as part of your C++ compilation process.

For example, your project’s CMake configuration might include:

include_directories($TENSORRT_LLM_PATH/cpp/include) link_directories($TENSORRT_LLM_PATH/cpp/build/tensorrt_llm) target_link_libraries(your_project tensorrt_llm)

This integration allows you to take advantage of the TensorRT-LLM optimizations in your custom C++ projects, ensuring efficient inference even in low-level or high-performance environments.

Advanced TensorRT-LLM Features

TensorRT-LLM is more than just an optimization library; it includes several advanced features that help tackle large-scale LLM deployments. Below, we explore some of these features in detail:

1. In-Flight Batching

Traditional batching involves waiting until a batch is fully collected before processing, which can cause delays. In-Flight Batching changes this by dynamically starting inference on completed requests within a batch while still collecting other requests. This improves overall throughput by minimizing idle time and enhancing GPU utilization.

This feature is particularly valuable in real-time applications, such as chatbots or voice assistants, where response time is critical.

2. Paged Attention

Paged Attention is a memory optimization technique for handling large input sequences. Instead of requiring contiguous memory for all tokens in a sequence (which can lead to memory fragmentation), Paged Attention allows the model to split key-value cache data into “pages” of memory. These pages are dynamically allocated and freed as needed, optimizing memory usage.

Paged Attention is critical for handling large sequence lengths and reducing memory overhead, particularly in generative models like GPT and LLaMA.

3. Custom Plugins

TensorRT-LLM allows you to extend its functionality with custom plugins. Plugins are user-defined kernels that enable specific optimizations or operations not covered by the standard TensorRT library.

For example, the Flash-Attention plugin is a well-known custom kernel that optimizes multi-head attention layers in Transformer-based models. By using this plugin, developers can achieve substantial speed-ups in attention computation—one of the most resource-intensive components of LLMs.

To integrate a custom plugin into your TensorRT-LLM model, you can write a custom CUDA kernel and register it with TensorRT. The plugin will be invoked during model execution, providing tailored performance improvements.

4. FP8 Precision on NVIDIA H100

With FP8 precision, TensorRT-LLM takes advantage of NVIDIA’s latest hardware innovations in the H100 Hopper architecture. FP8 reduces the memory footprint of LLMs by storing weights and activations in an 8-bit floating-point format, resulting in faster computation without sacrificing much accuracy. TensorRT-LLM automatically compiles models to utilize optimized FP8 kernels, further accelerating inference times.

This makes TensorRT-LLM an ideal choice for large-scale deployments requiring top-tier performance and energy efficiency.

Example: Deploying TensorRT-LLM with Triton Inference Server

For production deployments, NVIDIA’s Triton Inference Server provides a robust platform for managing models at scale. In this example, we will demonstrate how to deploy a TensorRT-LLM-optimized model using Triton.

Step 1: Set Up the Model Repository

Create a model repository for Triton, which will store your TensorRT-LLM model files. For instance, if you have compiled a GPT2 model, your directory structure might look like this:

mkdir -p model_repository/gpt2/1 cp ./trt_engine/gpt2_fp16.engine model_repository/gpt2/1/

Step 2: Create the Triton Configuration File

In the same model_repository/gpt2/ directory, create a configuration file named config.pbtxt that tells Triton how to load and run the model. Here’s a basic configuration for TensorRT-LLM:

name: "gpt2" platform: "tensorrt_llm" max_batch_size: 8 input [ name: "input_ids" data_type: TYPE_INT32 dims: [-1] ] output [ name: "logits" data_type: TYPE_FP32 dims: [-1, -1] ]

Step 3: Launch Triton Server

Use the following Docker command to launch Triton with the model repository:

docker run --rm --gpus all -v $(pwd)/model_repository:/models nvcr.io/nvidia/tritonserver:23.05-py3 tritonserver --model-repository=/models

Step 4: Send Inference Requests to Triton

Once the Triton server is running, you can send inference requests to it using HTTP or gRPC. For example, using curl to send a request:

curl -X POST http://localhost:8000/v2/models/gpt2/infer -d ' "inputs": [ "name": "input_ids", "shape": [1, 128], "datatype": "INT32", "data": [[101, 234, 1243]] ] '

Triton will process the request using the TensorRT-LLM engine and return the logits as output.

Best Practices for Optimizing LLM Inference with TensorRT-LLM

To fully harness the power of TensorRT-LLM, it’s important to follow best practices during both model optimization and deployment. Here are some key tips:

1. Profile Your Model Before Optimization

Before applying optimizations such as quantization or kernel fusion, use NVIDIA’s profiling tools (like Nsight Systems or TensorRT Profiler) to understand the current bottlenecks in your model’s execution. This allows you to target specific areas for improvement, leading to more effective optimizations.

2. Use Mixed Precision for Optimal Performance

When optimizing models with TensorRT-LLM, using mixed precision (a combination of FP16 and FP32) offers a significant speed-up without a major loss in accuracy. For the best balance between speed and accuracy, consider using FP8 where available, especially on the H100 GPUs.

3. Leverage Paged Attention for Large Sequences

For tasks that involve long input sequences, such as document summarization or multi-turn conversations, always enable Paged Attention to optimize memory usage. This reduces memory overhead and prevents out-of-memory errors during inference.

4. Fine-tune Parallelism for Multi-GPU Setups

When deploying LLMs across multiple GPUs or nodes, it’s essential to fine-tune the settings for tensor parallelism and pipeline parallelism to match your specific workload. Properly configuring these modes can lead to significant performance improvements by distributing the computational load evenly across GPUs.

Conclusion

TensorRT-LLM represents a paradigm shift in optimizing and deploying large language models. With its advanced features like quantization, operation fusion, FP8 precision, and multi-GPU support, TensorRT-LLM enables LLMs to run faster and more efficiently on NVIDIA GPUs. Whether you are working on real-time chat applications, recommendation systems, or large-scale language models, TensorRT-LLM provides the tools needed to push the boundaries of performance.

This guide walked you through setting up TensorRT-LLM, optimizing models with its Python API, deploying on Triton Inference Server, and applying best practices for efficient inference. With TensorRT-LLM, you can accelerate your AI workloads, reduce latency, and deliver scalable LLM solutions to production environments.

For further information, refer to the official TensorRT-LLM documentation and Triton Inference Server documentation.

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Выпущена Stable Diffusion 3 Medium – топовая нейросеть для создания картинок из текста Stability AI объявила о выпуске Stable Diffusion 3 Medium. Разработчики называют ее «самой сложной моделью генерации изображений на сегодняшний день». Информация о нейронке и ее возможностях появилась на сайте Stability AI. Stable Diffusion 3 Medium — это самая продвинутая открытая модель Stability AI для преобразования текста в изображение. Небольшой размер этой модели делает ее идеальной для работы на обычных ПК и ноутбуках, а также на графических процессорах корпоративного уровня. Ее размер подходит для того, чтобы стать следующим стандартом в моделях преобразования текста в изображение. Опробовать возможности Stable Diffusion 3 Medium бесплатно можно на Hugging Face. Также можно использовать API на Stability P[ подпи]с]аться на бесплатную трехдневную пробную версию Stable Assistant или попробовать Stable Artisan через Discord. Новая версия ИИ-генератора изображений работает с 2 млрд параметров. На основе текстового запроса она создает качественные и фотореалистичные картинки с хорошей детализацией, цветом и освещением. Успех в устранении распространенных ошибок других моделей, таких как нереалистичные руки и лица, достигается за счет таких инноваций, как 16-канальный VAE. Кроме того, разработка быстрее аналогов понимает длинные и сложные подсказки, учитывая пространственное мышление, композиционные элементы, действия и стили. ресурсоемкость тоже не подкачала. Нейронка идеально подходит для работы на стандартных потребительских графических процессорах без снижения производительности благодаря небольшому объему видеопамяти. Во время создания Stable Diffusion 3 Medium разработчики сотрудничали с NVIDIA и AMD для повышения производительности ИИ-модели. В частности, Stability AI упоминает работу с графическими процессорами NVIDIA RTX и TensorRT. «Мы планируем постоянно совершенствовать Stable Diffusion 3 Medium на основе отзывов пользователей, расширять ее возможности и повышать производительность. Наша цель — установить новый стандарт творчества в искусстве, генерируемом искусственным интеллектом, и сделать Stable Diffusion 3 Medium жизненно важным инструментом как для профессионалов, так и для любителей», — комментирует команда Stability AI.