#Chip Design

[jcmarchi]

Cerebras Inference and the Challenges of Challenging NVIDIA’s Dominance

New Post has been published on https://thedigitalinsider.com/cerebras-inference-and-the-challenges-of-challenging-nvidias-dominance/

Cerebras Inference and the Challenges of Challenging NVIDIA’s Dominance

Why does NVIDIA remains virtually unchallenged in the AI chip market?

Created Using Ideogram

Next Week in The Sequence:

Edge 427: Our series about state space models(SSM) continues with a review of AI21’s Jamba, a model that combines transformers and SSMs. We discuss Jamba’s original research paper and the DeepEval framework.

Edge 428: We dive into PromptPoet, Character.ai’s framework for prompt optimization.

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📝 Editorial: Cerebras Inference and the Challenges of Challenging NVIDIA’s Dominance

AI hardware is experiencing an innovation renaissance, with well-funded startups emerging everywhere. Yet, NVIDIA remains virtually unchallenged, holding close to a 90% share of the AI chip market. Why is that?

We’ve all heard explanations about the advantages of NVIDIA’s software stack for acceleration compared to platforms like AMD’s, which seems like a lazy explanation for why NVIDIA is out-innovating its competitors. A simple theory that I’ve discussed with several scientists and engineers who pretrain large foundation models is that NVIDIA is the only platform receiving regular feedback about the performance of chips during pretraining runs with tens of thousands of GPUs. It turns out that at that scale, many challenges arise that are nearly impossible to simulate on a smaller scale. I will elaborate more on that theory in a future post, but the main point is that there is a very high barrier to entry when it comes to challenging NVIDIA chips for pretraining. The only viable candidate seems to be Google TPUs, which have definitely been tested at massive scale.

If pretraining is out of the equation, the obvious area to explore is inference. Here, we have a completely different playing field, where performance optimizations can be applied at a smaller scale, making it more conducive to startup disruptions.

One of the viable challengers to NVIDIA’s dominance in AI inference is Cerebras. Just last week, the well-funded startup unveiled Cerebras Inference, a solution capable of delivering Llama 3.1 8B at 450 tokens per second for Llama 3.1 70B. This is approximately 20x faster than NVIDIA GPUs and about 2.4x faster than Groq. The magic behind Cerebras’ performance is its AI chip design, which allows the entire model to be stored on-chip, eliminating the need for GPU communication.

Cerebras Inference looks impressive from top to bottom and clearly showcases the massive potential for innovation in AI inference. Competing with NVIDIA will require more than just faster chips, but Cerebras appears to be a legitimate challenger.

🔎 ML Research

The Mamba in the Llama

Researchers from Princeton University, Together AI, Cornell University and other academic institutions published a paper proposing a technique to distill and accelerate transformer-SSM models. The method distills transformers into RNN-equivalents with a quarter of the hidden layers —> Read more.

Diffusion Models as Real Time Game Engines

Google Research published a paper presenting GameNGen, a game engine powered by diffusion models and interactions with real environments over long trajectories. GameNGen can simulate a DOOM game in over 20 frames in a single TPU —> Read more.

LLMs that Learn from Mistakes

Researchers from Meta FAIR and Carnegie Mellon University published a paper outlining a technique to include error-correction data directly in the pretraining stage in order to improve reasoning capabilities. The resulting model outperform alternatives trained in error-free data —> Read more.

Table Augmented Generation

In a new paper, Researchers from UC Berkeley proposed table augmented generation, a method that addresses some of the limitations of text-to-SQL and RAG and answer questions in relational databases. The TAG model captures a very complete sets of interaction between an LLM and a database —> Read more.

DisTrO

Nous Research published a paper introducing DisTrO, an architecture that reduces inter-GPU communication by up to 5 orders of magnitude. DisTrO is an important method for low latency training of large neural networks —> Read more.

Brain Inspired Design

Microsoft Research published a summary of their recent research in three projects that simualte the brain learns. One project simulates the brain computes information, another enhances accuracy and efficiency and the third one shows improves proficiency in language processing and pattern recognition —> Read more.

🤖 AI Tech Releases

Qwen2-VL

Alibaba Research released a new version of Qwen2-VL their marquee vision language model —> Read more.

Cerebras Inference

Cerebras released an impressive inference solution that can generate 1800 tokens per second in Llama 3.1 models —> Read more.

NVIDIA NIM Blueprints

NVIDIA released NIM Blueprints, a series of templates to help enterprises get started with generative AI applications —> Read more.

Gemini Models

Google DeepMind released a new series of experimental models —> Read more.

Command R

Cohere released a new version of Command R with improvements in coding, math, reasoning and latency —> Read more.

🛠 Real World AI

Recommendations at Netflix

Netflix discusses some of the AI techniques to enhances long term satisfaction in their content recommendations —> Read more.

📡AI Radar

AI coding platform Magic raised an impressive $320 million round.

Another AI coding platform Codium raised $150 million in a Series C.

Amazon hired the founders of Robotis startup Covariant.

Midjourney announced their intentions of going into hardware.

OpenAI is closing its tender offer at $100 billion valuation.

OpenAI and Anthropic agreed the U.S AI Institute to review and evaluate their models.

AI customer service platform Bland AI raised $16 million in new funding.

Inflection AI is porting its Pi chatbot to enterprise workflows.

Antrophic made its Artifacts solution available in IOS and Android.

Asset manager Magnetar Capital is launching its first venture fund focused on generative AI.

Atlassian acquired AI meeting bot company Rewatch.

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

[veer-acl]

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[learnandgrowcommunity]

Use this trick to Save time : HDL Simulation through defining clock

Why is this trick useful? Defining a clock in your simulation can save you time during simulation because you don't have to manually generate the clock signal in your simulation environment. Wanted to know how to define and force clock to simulate your digital system. Normally define clock used to simulate system with clock input. But I am telling you this trick for giving values to input ports other than clock. It will help you to save time in simulation because you do not need to force values to input ports every time. Lets brief What we did - gave some clock frequency to input A, like we gave 100. Than we made Half the frequency of clock to 50 and gave it to Input B. In similar way if we have 3rd input too we goanna half the frequency again to 25 and would give to next input.

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[alienssstufff]

ETHO - BDUBS - GEM Designs for Season 10!

They be pirates :]

Every new smp/series I come across- i like to assign an overarching theme to it and this hc season is no different:

The mainland of Season 10 is riddled with pirates and cowboys. Each hermit is wanted of some charge, for one reason or another. As initiation, losing a life will remove that bounty and that hermit can start fresh. But becoming the last survivor, that hermit will be gifted the Treasure of the Island (whatever the reward Grian says it is 😭)

[chiptrillino]

67?

(ID in ALT text) i know technically this can't be considered a wip. but it is wip to me because to this day i can't still pick ONE to post and i keep going back and forth.. and so it got doomed to stay in my folders just like this?

[dizzybizz]

gillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgillgill

[avephelis]

FINALLY RELEASING MAGICAL GIRL ALBATRIO UPON THE WORLD 🎉🎉 worked on these for @vyrion's madoka au, go check it out

[nautls11]

gillion and the tidestriders but make it a 90s metal band logo

[jcmarchi]

Groq’s $640 Million Boost: A New Challenger in the AI Chip Industry

New Post has been published on https://thedigitalinsider.com/groqs-640-million-boost-a-new-challenger-in-the-ai-chip-industry/

Groq’s $640 Million Boost: A New Challenger in the AI Chip Industry

In a significant development for the AI chip industry, startup Groq has secured a massive $640 million in its latest funding round. This financial windfall, led by investment giant BlackRock, has catapulted Groq’s valuation to an impressive $2.8 billion. The substantial investment signals strong confidence in Groq’s potential to disrupt the AI hardware market, currently dominated by industry titan Nvidia.

Groq, founded in 2016 by Jonathan Ross, a former Google engineer, has been quietly developing specialized chips designed to accelerate AI workloads, particularly in the realm of language processing. The company’s flagship product, the Language Processing Unit (LPU), aims to offer unprecedented speed and efficiency for running large language models and other AI applications.

As the demand for AI-powered solutions continues to soar across industries, Groq is positioning itself as a formidable challenger to established players. The company’s focus on inference – the process of running pre-trained AI models – could give it a unique edge in a market hungry for more efficient and cost-effective AI hardware solutions.

The Rise of Specialized AI Chips

The exponential growth of AI applications has created an insatiable appetite for computing power. This surge in demand has exposed the limitations of traditional processors in handling the complex and data-intensive workloads associated with AI.

General-purpose CPUs and GPUs, while versatile, often struggle to keep pace with the specific requirements of AI algorithms, particularly when it comes to processing speed and energy efficiency. This gap has paved the way for a new generation of specialized AI chips designed from the ground up to optimize AI workloads.

The limitations of traditional processors become especially apparent when dealing with large language models and other AI applications that require real-time processing of vast amounts of data. These workloads demand not only raw computational power but also the ability to handle parallel processing tasks efficiently while minimizing energy consumption.

Groq’s Technological Edge

At the heart of Groq’s offering is its innovative LPU. Unlike general-purpose processors, LPUs are specifically engineered to excel at the types of computations most common in AI workloads, particularly those involving natural language processing (NLP).

The LPU architecture is designed to minimize the overhead associated with managing multiple processing threads, a common bottleneck in traditional chip designs. By streamlining the execution of AI models, Groq claims its LPUs can achieve significantly higher processing speeds compared to conventional hardware.

According to Groq, its LPUs can process hundreds of tokens per second even when running large language models like Meta’s Llama 2 70B. This translates to the ability to generate hundreds of words per second, a performance level that could be game-changing for real-time AI applications.

Moreover, Groq asserts that its chips offer substantial improvements in energy efficiency. By reducing the power consumption typically associated with AI processing, LPUs could potentially lower the operational costs of data centers and other AI-intensive computing environments.

While these claims are certainly impressive, it’s important to note that Nvidia and other competitors have also made significant strides in AI chip performance. The real test for Groq will be in demonstrating consistent real-world performance advantages across a wide range of AI applications and workloads.

Targeting the Enterprise and Government Sectors

Recognizing the vast potential in enterprise and government markets, Groq has crafted a multifaceted strategy to gain a foothold in these sectors. The company’s approach centers on offering high-performance, energy-efficient solutions that can seamlessly integrate into existing data center infrastructures.

Groq has launched GroqCloud, a developer platform that provides access to popular open-source AI models optimized for its LPU architecture. This platform serves as both a showcase for Groq’s technology and a low-barrier entry point for potential customers to experience the performance benefits firsthand.

The startup is also making strategic moves to address the specific needs of government agencies and sovereign nations. By acquiring Definitive Intelligence and forming Groq Systems, the company has positioned itself to offer tailored solutions for organizations looking to enhance their AI capabilities while maintaining control over sensitive data and infrastructure.

Key partnerships and collaborations

Groq’s efforts to penetrate the market are bolstered by a series of strategic partnerships and collaborations. A notable alliance is with Samsung’s foundry business, which will manufacture Groq’s next-generation 4nm LPUs. This partnership not only ensures access to cutting-edge manufacturing processes but also lends credibility to Groq’s technology.

In the government sector, Groq has partnered with Carahsoft, a well-established IT contractor. This collaboration opens doors to public sector clients through Carahsoft’s extensive network of reseller partners, potentially accelerating Groq’s adoption in government agencies.

The company has also made inroads internationally, signing a letter of intent to install tens of thousands of LPUs in a Norwegian data center operated by Earth Wind & Power. Additionally, Groq is collaborating with Saudi Arabian firm Aramco Digital to integrate LPUs into future Middle Eastern data centers, demonstrating its global ambitions.

The Competitive Landscape

Nvidia currently stands as the undisputed leader in the AI chip market, commanding an estimated 70% to 95% share. The company’s GPUs have become the de facto standard for training and deploying large AI models, thanks to their versatility and robust software ecosystem.

Nvidia’s dominance is further reinforced by its aggressive development cycle, with plans to release new AI chip architectures annually. The company is also exploring custom chip design services for cloud providers, showcasing its determination to maintain its market-leading position.

While Nvidia is the clear frontrunner, the AI chip market is becoming increasingly crowded with both established tech giants and ambitious startups:

Cloud providers: Amazon, Google, and Microsoft are developing their own AI chips to optimize performance and reduce costs in their cloud offerings.

Semiconductor heavyweights: Intel, AMD, and Arm are ramping up their AI chip efforts, leveraging their extensive experience in chip design and manufacturing.

Startups: Companies like D-Matrix, Etched, and others are emerging with specialized AI chip designs, each targeting specific niches within the broader AI hardware market.

This diverse competitive landscape underscores the immense potential and high stakes in the AI chip industry.

Challenges and Opportunities for Groq

As Groq aims to challenge Nvidia’s dominance, it faces significant hurdles in scaling its production and technology:

Manufacturing capacity: Securing sufficient manufacturing capacity to meet potential demand will be crucial, especially given the ongoing global chip shortage.

Technological advancement: Groq must continue innovating to stay ahead of rapidly evolving AI hardware requirements.

Software ecosystem: Developing a robust software stack and tools to support its hardware will be essential for widespread adoption.

The Future of AI Chip Innovation

The ongoing innovation in AI chips, spearheaded by companies like Groq, has the potential to significantly accelerate AI development and deployment:

Faster training and inference: More powerful and efficient chips could dramatically reduce the time and resources required to train and run AI models.

Edge AI: Specialized chips could enable more sophisticated AI applications on edge devices, expanding the reach of AI technology.

Energy efficiency: Advances in chip design could lead to more sustainable AI infrastructure, reducing the environmental impact of large-scale AI deployments.

As the AI chip revolution continues to unfold, the innovations brought forth by Groq and its competitors will play a crucial role in determining the pace and direction of AI advancement. While challenges abound, the potential rewards – both for individual companies and for the broader field of artificial intelligence – are immense.

[veer-acl]

In recent years, the semiconductor industry has witnessed a significant transformation, primarily due to integrating artificial intelligence (AI) into various semiconductor design, production, and testing aspects. The semiconductor sector catalyzes technological progress, fueling the development of devices that have become essential in contemporary living. With the increasing need for faster, smaller, and more energy-efficient chips, the industry encounters fresh hurdles in downsizing conventional manufacturing procedures.

[radiumjuice]

eh. suit could be weirder looking. I’ll revise it later maybe

[fpacini]

Interesting article from ars technica. Love the photo of the snail on the cpu :-D

[under-lok-n-ki]

saying a mean thing DOES hurt more gilly has a point

[chiptrillino]

I'm sorry, just sent in 62 for the ask game because I'd overlooked that it's the amazing scifi thing!

Soooo 63?

(ID in ALT text)

okay so... you know whats really funny here. i think made this while for the first time polls popped up on tumblr. and i had this werid idea of like... "choose your adventure" kind of story telling. but... lets be honest i don't have much... time to draw all the options? but this is still like... a sort of darker AU which is deer to my heart. and till today i don't know if they should have a happy end or not. sokka got shipwrecked. and to make his situation even worse zuko poped up and and took a bite of him.

the whole siren idea is more based on the sinister one. the one drowning and eating seamen. and zuko is now out to eat sokka. i have some plot lines written out. -sokka playing with zuko a game of riddles to buy himself some more time -zuko being unable to stay in the sun so sokka has to decide if he lest him take shelter underneath his make shift raft or lure him out of there to burn him. at the end sokka does get saved and can escape for some time? because zuko did end up biting him. and now sokka cant stop hearing him in his head, and zuko can still follow him. so... watch out sokka! zuko is on his way to eat you up but i still don't know if he means it literally or in a more plessurable way...

[wasyago]

jay and chip as tritons hehe

(i know that every other person drew them as tritons already, but like, cmon its so fun)

[mayanhandballcourt]

Photographer Jake Michaels