एक chip company जो wafer-scale AI processors build करती है — एक पूरे silicon wafer के size के chips, एक standard GPU से 100x से ज़्यादा बड़े। Cerebras WSE-3 (Wafer Scale Engine) 4 trillion transistors और 900,000 cores रखता है। उनके CS-3 systems training और inference दोनों के लिए designed हैं, thousands of individual GPUs के clusters का एक alternative offer करते हुए।
यह क्यों matter करता है
Cerebras AI hardware का सबसे radical rethinking represent करता है। Thousands of small chips को limited bandwidth से connect करने के बजाय, वो सब कुछ एक massive chip पर रखते हैं enormous on-chip memory bandwidth के साथ। Potential advantage ये है कि multi-GPU training को limit करने वाली communication bottleneck eliminate हो जाती है। क्या wafer-scale computing NVIDIA के massive ecosystem के साथ compete कर सकती है, ये billion-dollar question है।
Deep Dive
The WSE-3 has 44 GB of on-chip SRAM — not HBM or DRAM, but SRAM directly on the compute die. This provides ~21 PB/s of memory bandwidth, orders of magnitude more than GPU HBM bandwidth. For memory-bandwidth-bound operations (like LLM inference, which is limited by how fast you can read model weights), this is a fundamental advantage. The trade-off: 44 GB of on-chip memory can't hold the largest models, requiring model-parallel strategies across multiple CS-3 systems.
Inference Speed
Cerebras has demonstrated impressive inference speeds — serving Llama-70B at over 2,000 tokens/second, competitive with or exceeding Groq's LPU. The approach is different (wafer-scale chip vs. deterministic ASICs) but the result is similar: purpose-built hardware that dramatically outperforms GPUs for the specific workload of LLM token generation.