vh
6851ecbd0b
meromero-rp sized its KV from --gpu-memory-utilization, and vLLM's profile counts co-booting neighbours as its own non-torch memory. Three boots gave 20.37 / 26.74 / 29.04 GiB of KV, and the 2026-10-05 Docker-move restart cut GPU 1 free memory to 3,981 MiB, below intern-decision's 32k-token budget. Coder read negative KV on every co-boot. Both are now pinned, so util only gates the boot check (meromero util 0.52 -> 0.47, just above its footprint). GPU 0 plateaus at ~60 MiB free 1-2 days after a boot (Beszel), so gen-small's KV drops to 5.5 GiB (461,215 tokens, 1.76x) and parakeet-nemo stays on GPU 0 with room for its 3,840 MiB cap. GPU 3 stays clear. Applied with playbooks/fv-ml1-kv-pin-20261005.yaml: GPU 1 free 3,981 -> 11,801 MiB, GPU 0 495 -> 5,039 MiB right after boot; all nine seats healthy.
101 lines
5.4 KiB
Bash
101 lines
5.4 KiB
Bash
# vllm stack tunables. Copy this to `.env` on the server before deploying.
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#
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# cp .env.example .env
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# # edit .env with real values
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# docker compose up -d
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# Image version — PINNED. Do not put `latest` here: every service in this file
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# shares this one variable, so a bare `docker compose up -d` would silently
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# upgrade the whole stack's vLLM at once. Pinned 2026-08-20 to the version all
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# seats were already running (`latest` and `v0.24.0` were the same local image,
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# 4091d5593f77, so the pin changed nothing at runtime). Bump deliberately.
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VLLM_VERSION=v0.24.0
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# Host ports (container always listens on 8000 internally)
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# (EMBED_PORT 8001 / RERANK_PORT 8013 retired 2026-09-25 with their seats —
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# embedding + reranking moved to TEI on esh-ml1, stacks/embed-rerank.)
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# REWARD_PORT=8003 (retired here 2026-09-25 — the reward seat moved to esh-ml1, stacks/reward-seat)
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# GPU assignment — all services share this GPU
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# (ana-ml2 has 0 and 1; default 1 keeps 0 free for heavy LLM work in llama-swap)
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GPU_ID=1
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# Models
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# Skywork is a local-path AWQ output, not from HF Hub. Bind-mounted into the
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# reward container at /local-models — see compose.yaml. No env var here for
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# the model path itself since it's hard-coded in the compose command.
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# GPU memory split — fractions are of TOTAL GPU memory, not free memory.
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# vLLM profiles each service independently, so each slice must be large enough
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# to fit that service's model + KV cache with no awareness of the others.
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# Setting any one too low causes that container to OOM on KV cache allocation
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# with `Available KV cache memory: -X.XX GiB`.
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#
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# Layout on a 48 GB Ada, RIGHT-SIZED 2026-06-05 to free room for the granite
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# summarizer's CUDA graphs + a LoRA hedge. The trio was wildly over-provisioned:
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# embed ran a 5.89x KV pool, reward 3.90x — pure waste for utility models that
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# need ~1.5x. Trimmed to free ~10 GB. (Utilization = fraction of TOTAL GPU mem;
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# each service profiles independently. Too low → that container OOMs on KV with
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# `Available KV cache memory: -X.XX GiB`.)
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# EMBED 0.07 (~3.4 GB) — 0.6B Qwen3 embed @8k; 1.1GB weights + ~2GB KV (~3x)
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# RERANK 0.07 (~3.4 GB) — 0.6B Qwen3 rerank @8k; comfortable
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# REWARD 0.18 (~8.6 GB) — 8B Skywork AWQ @16k; 4.4GB weights + ~3.5GB KV (~1.5x)
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#
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# RETUNED 2026-06-12 for the Ada->Blackwell swap (cards are now 96 GB, so the
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# Ada-era fractions reserved ~2x the bytes for the same models). Empirically
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# re-floored: util climbed in 0.01 steps until each service was stable under
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# 20x parallel inference.
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# embed/rerank 0.05 (~4.8 GB ea) — the load-floor for the 0.6B models (0.04
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# OOMs on engine init; their footprint is fixed weights+context, not KV).
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# reward 0.10 (~9.6 GB) — the real over-provision was here (0.18 -> 0.10).
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# Net: ~11 GB freed on GPU 1. (Fractions are of TOTAL card VRAM — re-floor if
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# the cards change again.)
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# 0.03 each — REBALANCED 2026-06-14 (was 0.05): the 0.6B models sat at ~5.5 GB
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# each at 0.05 (mostly util-reservation waste); 0.03 (~3.6 GB) fits weights +
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# CUDA context with room, freeing ~4 GB back to granite. Recreate them ONE AT A
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# TIME — concurrent recreate races the memory-profiling assertion.
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# REWARD_GPU_MEM_UTIL=0.10 (retired here 2026-09-25 — the reward seat moved to esh-ml1, stacks/reward-seat)
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# Context length caps — lower these if VRAM is tight.
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# Qwen3-Embedding supports up to 32k; reranker up to 32k.
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# Skywork capped at 16k server-side as defense-in-depth; JudgeClient also
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# enforces the cap at dispatch time per spec.
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# REWARD_MAX_MODEL_LEN=16384 (retired here 2026-09-25 — the reward seat moved to esh-ml1, stacks/reward-seat)
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# Optional API key — leave blank for no auth (fine on the internal network).
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# If set, all three services require `Authorization: Bearer <key>`.
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API_KEY=
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# HuggingFace token — only needed for gated models in the HF-Hub-loaded
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# services (embed/rerank). Reward is local-path, ignores this.
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HF_TOKEN=
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# === granite-4.1-8b — RETIRED 2026-08-12, vars removed 2026-08-20 ===
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# Was the production summarizer. The `summarizer` / `classifier` gateway aliases
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# were repointed at the gen seat and the container stopped; the service block and
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# these tunables are now gone. See the tombstone in compose.yaml for the lesson
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# this cost us (nevermore stayed pinned to the dead `granite-4.1-8b` alias and
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# failed silently for 8 days).
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# Qwen2.5-Coder-1.5B (BASE) — FIM code-completion seat for Zed editor inline
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# edit-predictions (deep-research pick 2026-07-27, Apache-2.0). GPU-1, alongside the
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# small-model trio + (phasing-out) granite. Reached via LiteLLM `coder-fast` alias
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# and the keyless zed-fim-proxy (stacks/zed-fim-proxy). util 0.06 (~5.7 GB) holds
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# the 1.5B fp16 + fp8 KV; 8192 ctx is ample for FIM (KV = 13.75x concurrency).
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CODER_PORT=8020
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CODER_GPU_ID=1
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CODER_MODEL=Qwen/Qwen2.5-Coder-1.5B
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CODER_SERVED_NAME=qwen2.5-coder-1.5b
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CODER_MAX_MODEL_LEN=8192
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CODER_KV_CACHE_DTYPE=fp8
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CODER_GPU_MEM_UTIL=0.055
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# KV pinned 2026-10-05 (77,056 tokens; see compose.yaml). Util now only gates the boot check.
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CODER_KV_CACHE_MEMORY=1104674816
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CODER_MAX_NUM_SEQS=32
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# === LFM2.5-2.6B — RETIRED PERMANENTLY 2026-08-20 (operator directive) ===
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# Eval-only bake-off seat vs granite-4.1-8b that never got its production ruling;
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# its comparator was retired first, and it logged 0 calls in the 4 days before it
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# came down. Container removed, service block and tunables deleted, gateway alias
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# dropped. Weights remain in the shared HF cache.
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