LLM inference can switch from compute-bound to memory-bound during a single request.
This is a dev post classified by AGK Intelligence as Hosting & infra (a tutorial), kept by the Dev Radar because it carries real work, not commentary.
LLM inference can switch from compute-bound to memory-bound during a single request. The same model, running on the same GPU, can encounter very different performance limits as it moves from processing the prompt to generating output tokens. During prefill, the model processes the input tokens in parallel and builds the KV cache. Long-prompt prefill is often compute-limited because of the large matrix operations involved. Decode behaves differently. Tokens are generated autoregressively, and each step reuses model weights and accesses the growing KV cache. At low batch sizes, memory bandwid
Posted by Tech with Mak (48.6k followers) 1 h ago · 25 likes · 923 views · view the original post on X. Kept by the Dev Radar as Hosting & infra.
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