📰 Prefill, Decode ve KV Cache: LLM’lerin Hızını Artıran 3 Gizli Süreç (2026 Verileriyle) Büyük dil
| Source: Mastodon | Original article
A joint research effort by the Nordic Institute for AI Systems and IBM’s Fusion HCI team released a detailed analysis of large‑language‑model (LLM) inference pipelines, revealing how three often‑overlooked stages—prefill, decode and key‑value (KV) cache management—drive the bulk of latency and cost in production deployments. Using a corpus of 2026 inference logs from over 12 million API calls across OpenAI, Anthropic and Meta models, the study quantifies the time spent in each phase, shows how KV‑cache fragmentation inflates memory bandwidth, and demonstrates that a semantic‑aware scheduler can shave up to 35 % off end‑to‑end response times without sacrificing throughput.
The findings matter because inference expense remains the dominant line item for AI‑driven services. By isolating the prefill stage—where the prompt is tokenised and the KV cache is populated—from the decode stage—where tokens are generated sequentially—the authors prove that aggressive batching in prefill and speculative decoding in decode can be combined with dynamic cache warm‑up to reduce both time‑to‑first‑token (TTFT) and inter‑token latency (ITL). Their KV‑cache algorithm, which re‑uses embeddings from semantically similar prompts, cuts VRAM reads by 40 % and lowers power draw, a boon for edge‑centric applications and for organisations grappling with the $0.02‑$0.05 per‑token price tags seen in recent Anthropic and OpenAI pricing.
What to watch next is how quickly cloud providers and open‑source inference stacks adopt these techniques. vLLM and the emerging llm‑d scheduler already hint at integration, but broader rollout will depend on hardware support—particularly the next‑gen tensor cores IBM promises for 2027—and on standardising KV‑cache APIs across frameworks. If the industry embraces the paper’s recommendations, the next wave of AI products could deliver ChatGPT‑level responsiveness at a fraction of today’s cost.
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