Recent claims by Cerebras about their AI inference performance advantages over NVIDIA's GPUs have sparked intense discussion in the tech community, highlighting both potential breakthroughs and significant challenges in the AI hardware landscape.
Memory Limitations Raise Concerns
A critical point raised by the community is Cerebras' limited SRAM capacity. While the company boasts 44GB of SRAM in their CS-3 system, this proves insufficient for larger models. As user 'menaerus' points out:
CS-1 had 18G of SRAM, CS-2 extended it to 40G and CS-3 has 44G of SRAM. None of these is sufficient to run the inference of Llama 70B and much less so of even larger models.
Cost-Performance Trade-offs
The economics of Cerebras' solution have drawn particular scrutiny. At $900 million for 576 CS-3 nodes ($1.56 million per node), the cost structure appears challenging. Community analysis reveals:
- 4 CS-3 nodes ($6.24M) are required to serve one 70B model
- Comparable AMD MI300x cluster (~$5M) can serve multiple models with 24,576GB combined memory
- Google Cloud's TPU v5e offers 2,175 tokens/second on Llama2 70B at approximately $100K per year
Competitive Landscape
AMD and Google emerge as strong contenders:
- AMD MI300x offers 192GB HBM3 memory per unit
- MI325x will provide 256GB HBM3e
- Google's newly announced TPU v6 promises 4x improvement in training performance and 3x increase in inference throughput
Niche Market Potential
Despite limitations, Cerebras may find success in specific use cases. As 'krasin' notes, their technology could be valuable for low-latency feedback: audio chat with LLM, robotics, etc. However, this represents a narrow segment of the overall AI market.
The community consensus suggests that while Cerebras shows promising performance in certain scenarios, memory limitations and high costs may restrict its broader market adoption. The company's strategy of subsidizing cloud usage rates versus hardware sales has also raised questions about long-term business sustainability.