MoE Decoding Bandwidth Bottlenecks and Hardware Choices
AccBalanced · x · 2026-07-12
Discussing Groq's acquisition, this post touches on inference-side hardware design and MoE decoding bottlenecks. The author argues that MoE decode is heavily bandwidth-bound, and combining robust networking with distributed SRAM usage offers excellent cost-performance.
They separate the storage needs for prefill and decode: prefill is more FLOPS-bound and can use cheaper GDDR7 in some scenarios, but attention decode still requires HBM. The conclusion is that future hardware and inference systems must be co-designed; Groq's approach is better suited for MoE decode, whereas Cerebras represents a different, more broadly applicable chip architecture.
Related event: AI Inference Split: Compute for Prefill, Bandwidth for Decode(2 posts)→
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