Revolutionizing AI Inference: How Groq, Cerebras, and SambaNova Are Shaping the Future
OpenAI’s introduction of the o1 model series, featuring advanced reasoning capabilities, has reignited discussions about inference times in AI. Unlike traditional Large Language Models (LLMs) that prioritize rapid responses, o1 takes a more deliberate approach. It employs multiple sequential inferences to generate more accurate answers, albeit at the cost of increased response time.
This methodology bears similarities to existing agent-based frameworks built on traditional LLMs, which also utilize multiple inference rounds. Both o1 and agent-based systems require high-speed inference LLMs and powerful hardware to operate effectively.
Consequently, this shift has brought renewed attention to Nvidia’s emerging competitors in the AI hardware space, particularly Groq, Cerebras, and SambaNova. These companies are at the forefront of innovation in AI acceleration technology.
In the following discussion, we’ll explore how these three companies are transforming the landscape of AI acceleration and consider the implications for the industry’s future.
The Innovators: A Technological Trifecta
Groq: Speed and Simplicity
Groq burst onto the scene with a focus on simplicity and deterministic execution. Their claim to fame? The fastest AI chip on the market, boasting an impressive SRAM bandwidth of 80TB/s — ten times that of Nvidia’s H100. However, Groq’s Achilles’ heel might be its limited per-chip SRAM capacity of 230MB, potentially explaining why they haven’t yet offered the 405B Llama-3.1 through their API. As it may require thousands of chips to even host an LLM of that size, Groq fails the scalability test.
Cerebras: The Wafer-Scale Wonder
Cerebras made waves with its Wafer-Scale Engine, a technological marvel that integrates an entire wafer into a single chip. This approach yields unprecedented computational power, backed by a staggering 44GB of SRAM and a mind-bending bandwidth of 21 Petabytes/s. On paper they look quite scalable. So, I wonder why their API doesn’t have Llama-3.1–405B yet. Probably they are still working on optimizing their hardware to host such a large model.
SambaNova: Flexible and Scalable
SambaNova takes a different tack with its SN40L AI Chips, utilizing a dataflow architecture. This innovative design allows for efficient processing by dynamically reconfiguring hardware resources based on task requirements. Their three-tier memory system, combining SRAM (fast but expensive), HBM (used in Nvidia GPUs), and conventional DRAM (slow but cheap), gives them a potential edge in scalability. Their recent announcement of Llama-3.1–405B on their API has made them the early leaders in the race for scalability.
Performance Showdown: Who’s Leading the Pack?
In the benchmarks, Cerebras blows everyone out of the water with a lightning fast 2011 tokes per second for Llama-3.1–8B. Mind you, a human can only read about 5 tokens in a second. SambaNova comes in 2nd with 988 and Groq 3rd with 750. Traditional Nvidia hardware in Azure or AWS, are lagging way behind with less than 100 t/s while being significantly more expensive.
For the larger Llama-3.1–70B, the story remains the same with Cerebras outperforming everyone.
When considering the largest Llama model, Llama-3.1–405B, SambaNova emerges as the clear leader as neither Cerebras nor Groq currently host it. While Cerebras may potentially host it in the future, Groq’s prospects seem less likely. SambaNova recently made headlines by achieving a new record for inference on this model, generating an impressive 132 output tokens per second while maintaining native 16-bit precision.
Looking at the abysmal performance of the Nvidia-based systems here, one wonders whether Nvidia is taking the competition seriously. Nvidia’s future direction is uncertain: will they continue with their HBM-based GPUs or explore the potential of SRAM to boost performance? However, none of the 3 competitors has the might to challenge the behemoth that is Nvidia. Therefore, it will be intriguing to see if Nvidia chooses to acquire one of these companies or develop their own high-performance inference architecture.
The Road Ahead: Shaping the Future of AI
The innovations brought forth by Groq, Cerebras, and SambaNova are poised to have a profound impact on the GenAI industry. As we witness a shift in compute demands from training to inference, companies specializing in inference hardware stand to benefit significantly.
Conclusion: A New Era of AI Acceleration
In the high-stakes race for AI inference supremacy, Groq, Cerebras, and SambaNova are pushing the boundaries of what’s possible. While SambaNova currently leads in some key metrics, particularly for large-scale models, Groq and Cerebras continue to innovate and compete fiercely.
As these companies drive the industry forward, we can expect to see even more complex and capable AI systems emerge. The future of AI hardware is not just about raw power — it’s about efficiency, scalability, and the ability to adapt to the ever-changing demands of artificial intelligence.
Stay tuned as this technological arms race continues to unfold, reshaping the landscape of Generative AI and paving the way for breakthroughs we’ve yet to imagine.
