rock
03e3e8393a
The name now matches the model. `coder` serves
Qwen3-Coder-30B-A3B-Instruct, a 30.5B MoE with 3.3B active, quantized to
4-bit GPTQ. It is measured working on this node, not assumed.
Measured, on worker-1, driver 580.159.04, compute capability 7.0:
GET /v1/models -> id=coder, root=ModelCloud/Qwen3-Coder-30B-A3B-Instruct-GPTQMODEL-W4A16-A
POST /v1/chat/completions model=coder -> 200 in 3.18s,
"fn main() { println!(\"hi\"); }"
POST /v1/chat/completions model=reasoning -> 200 in 0.21s
Weights loaded in 15.61 GiB. torch.compile took 78s. The pod reached
1/1 Ready. `kubectl diff -f coder.yaml` returns empty, so the manifest
matches live state.
Two names are served on purpose: `coder` and `reasoning`. The rename
therefore needs no change to poimen-memory's LLM_MODEL, only its host.
That keeps the client working through the cutover.
Why not Qwen3-Coder-Next, the model in the original plan. It cannot run
here, and that is measured too. A v0.15.0 image on this free V100 gave:
TORCH 2.9.1+cu129
ARCH_LIST [] <- empty
RuntimeError: cudaGetDeviceCount() ... Error 803
fla: ModuleNotFoundError <- no GatedDeltaNet kernels
vLLM 0.15.0 requires compute capability 7.5 or higher and fails here
before it loads a model. Qwen3-Next's hybrid attention needs the `fla`
module, which the image does not ship. So this stays on the pinned
v0.11.0 image, which reports sm_70..sm_120 and works.
Qwen publishes this model as BF16 (61.1 GB) and FP8 only. V100 has no
FP8, and 61.1 GB does not fit 32 GB, so 4-bit GPTQ is the only form that
fits one card. The checkpoint is a community quant; the manifest says so
and names the reason.
reasoning.yaml stays listed until poimen-memory's LLM_CHAT_BASE moves
from reasoning-predictor to coder-predictor. Two GPUs are free, so coder
rolled out beside reasoning with no downtime.
119 lines
4.3 KiB
YAML
119 lines
4.3 KiB
YAML
apiVersion: serving.kserve.io/v1beta1
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kind: InferenceService
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metadata:
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name: coder
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namespace: llm-serving
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labels:
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app.kubernetes.io/name: llm-coder
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app.kubernetes.io/part-of: llm-serving
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annotations:
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serving.kserve.io/deploymentMode: RawDeployment
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spec:
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predictor:
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minReplicas: 1
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maxReplicas: 1
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nodeSelector:
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kubernetes.io/hostname: worker-1
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# Two GPUs are free, so this rolls out beside `reasoning` instead of over it.
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# Recreate still matters for the later swap: a rolling update needs a card the
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# old pod holds and would sit Pending forever.
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deploymentStrategy:
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type: Recreate
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containers:
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- name: kserve-container
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# Pinned to v0.11.0 on purpose. This is the last image measured to work on this
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# node: torch 2.8.0+cu128, arch list sm_70..sm_120, driver 580.159.04.
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# v0.15.0 was measured and FAILED here: arch list empty, and
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# `cudaGetDeviceCount()` raised Error 803 (unsupported driver combination)
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# before any model load. Do not raise this tag without re-running that probe.
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image: vllm/vllm-openai:v0.11.0@sha256:014a95f21c9edf6abe0aea6b07353f96baa4ec291c427bb1176dc7c93a85845c
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args:
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# Qwen3-Coder-30B-A3B-Instruct: 30.5B total, 3.3B active. Architecture is
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# Qwen3MoeForCausalLM, the same class as the Qwen3-30B-A3B already serving
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# here, so it takes the proven code path on sm70.
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#
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# Qwen ships this model only as BF16 (61.1 GB) and FP8 (no FP8 on Volta).
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# 4-bit GPTQ is the only form that fits one 32 GB card. This checkpoint is a
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# community quant: ModelCloud maintains GPTQModel, and the repo is complete
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# (config + tokenizer + 4 shards, 16.9 GB). Verified by loading, not by trust.
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#
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# Qwen3-Coder-Next (80B/3B, the hybrid-attention model) is NOT an option:
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# its GatedDeltaNet kernels need compute capability 7.5+ and the `fla` module,
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# and sm70 has neither. Measured, not assumed.
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- --model=ModelCloud/Qwen3-Coder-30B-A3B-Instruct-GPTQMODEL-W4A16-A
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# `reasoning` stays a served name so an existing client that sends
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# model=reasoning keeps working after the DNS rename. vLLM takes the names
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# as a space-separated list, so they go in as separate argv items.
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- --served-model-name
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- coder
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- reasoning
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- --quantization=gptq
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- --dtype=float16
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- --kv-cache-dtype=auto
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# Native window is 262144. 64k keeps the KV cache comfortable on one card;
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# raise only after measuring.
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- --max-model-len=65536
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- --tensor-parallel-size=1
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- --pipeline-parallel-size=1
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- --gpu-memory-utilization=0.90
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- --max-num-seqs=16
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- --max-num-batched-tokens=8192
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- --long-prefill-token-threshold=2048
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- --enable-chunked-prefill
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- --enable-prefix-caching
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- --reasoning-parser=qwen3
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- --enable-auto-tool-choice
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- --tool-call-parser=hermes
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- --host=0.0.0.0
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- --port=8080
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env:
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# Required on this hardware, not tuning:
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# - flashinfer's check_cuda_arch raises on compute capability 7.0,
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# so the sampler must stay off
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# - FlashInfer and FlashAttention-2 need sm75+, so Triton serves attention
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- name: VLLM_USE_FLASHINFER_SAMPLER
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value: "0"
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- name: VLLM_ATTENTION_BACKEND
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value: TRITON_ATTN
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- name: HF_HOME
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value: /mnt/models
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ports:
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- containerPort: 8080
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protocol: TCP
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readinessProbe:
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httpGet:
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path: /health
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port: 8080
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periodSeconds: 10
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startupProbe:
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httpGet:
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path: /health
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port: 8080
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# The coder checkpoint is a 16.9 GB cold download on first start, then
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# ~20 s of weight load and ~80 s of torch.compile. 80 x 15 s covers it.
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failureThreshold: 80
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periodSeconds: 15
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resources:
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requests:
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cpu: "4"
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memory: 12Gi
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nvidia.com/gpu: "1"
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limits:
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cpu: "8"
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memory: 36Gi
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nvidia.com/gpu: "1"
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volumeMounts:
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- mountPath: /mnt/models
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name: models
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- mountPath: /dev/shm
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name: shm
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runtimeClassName: nvidia
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volumes:
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- name: models
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persistentVolumeClaim:
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claimName: llm-models
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- name: shm
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emptyDir:
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medium: Memory
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sizeLimit: 2Gi
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