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The WSE-3 chip has 44GB of on-chip SRAM with 21 PB/s of bandwidth—6,000 times faster than an H100's 3.35 TB/s—but only about half the memory, making it well-suited for inference (where an AI produces output tokens) rather than training where chip-to-chip networking limitations become apparent.
Inference workloads have three distinct parts: prefill (highly parallelizable, compute-heavy), and two alternating decode steps (both memory-bandwidth bound, requiring reads from KV cache and model weights), meaning sustained token generation speed matters more than raw compute when everything fits on-chip memory.
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