103 lines
3.5 KiB
YAML
103 lines
3.5 KiB
YAML
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apiVersion: serving.kserve.io/v1beta1
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kind: InferenceService
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metadata:
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name: reasoning
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namespace: llm-serving
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labels:
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app.kubernetes.io/name: llm-reasoning
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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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# Every GPU card is allocated, so a rolling update deadlocks: the new pod needs a
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# card the old pod still holds and sits Pending forever. Recreate frees the card
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# first. The cost is a short gap in service for this model only.
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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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image: vllm/vllm-openai:v0.11.0@sha256:014a95f21c9edf6abe0aea6b07353f96baa4ec291c427bb1176dc7c93a85845c
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args:
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# Qwen3-30B-A3B is a 30.5B MoE with 3.3B active per token. On a V100 that matters:
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# the 32B dense model it replaces needed two cards for the same context, this needs
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# one, because its KV cache is 96 KiB/token against the dense model's 256 KiB.
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#
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# Measured on this cluster, one V100-32GB, 64k context: 15.55 GiB of weights,
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# 137k tokens of KV cache, 2.09x concurrency at 65536 tokens per request.
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- --model=Qwen/Qwen3-30B-A3B-GPTQ-Int4
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- --served-model-name=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 40960. YaRN factor 2.0 reaches 81920, so 64k is inside it.
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- --rope-scaling={"rope_type":"yarn","factor":2.0,"original_max_position_embeddings":40960}
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- --tensor-parallel-size=1
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- --pipeline-parallel-size=1
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- --max-model-len=65536
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- --gpu-memory-utilization=0.90
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- --max-num-seqs=8
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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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# Both are required on this hardware, not tuning:
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# - flashinfer's check_cuda_arch raises AttributeError on compute capability 7.0
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# (flashinfer/jit/core.py:55, minor.isdigit() on an int), so the sampler must stay off
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# - FlashInfer and FlashAttention-2 both need sm75+, so the Triton backend 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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# Weight load took 18s from cache and 148s on a cold download, and the first
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# torch.compile adds about 70s. 80 x 15s leaves ample room for a cold start.
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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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