117 lines
4.3 KiB
YAML
117 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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annotations:
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serving.kserve.io/deploymentMode: RawDeployment
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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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name: reasoning
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namespace: llm-serving
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spec:
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predictor:
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containers:
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- args:
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# dense Qwen3-32B-bnb-4bit retired: sm70/V100 bnb dequant kernel is
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# slow (no int4 tensor cores pre-Turing, dequant-then-fp16-matmul is
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# two kernel launches not one fused int4 GEMM), decode crawled at
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# ~2.5-10 tok/s and blew the gateway's request timeout regardless of
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# TP/PP config. bnb also outright rejects tensor-parallel-size>1 on
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# prequant checkpoints ("Please try with pipeline parallelism"),
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# which is why this went through a PP=2 detour first.
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# Switched to Qwen3.5-35B-A3B (MoE, GDN hybrid attention) GPTQ-Int4.
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# Requires vLLM >=0.17.0 -- v0.11.0 errors with "Model architectures
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# ['Qwen3_5MoeForConditionalGeneration'] are not supported for now."
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# moe_wna16 is the checkpoint's documented quantization kernel;
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# compute-capability requirement on sm70 is UNVERIFIED going in --
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# this rollout is the real test. --kv-cache-dtype stays auto, not
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# fp8_e5m2: V100 has no FP8 tensor cores at all (Hopper/Ada only),
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# hardware-blocked regardless of vLLM version. tool-call-parser
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# changed hermes -> qwen3_coder per the checkpoint's own README
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# example, not cosmetic. gpu-memory-utilization starts low (0.5)
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# since real VRAM footprint for this arch+quant combo is unknown;
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# raise once stable. Old OffloadingConnector kv-transfer-config
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# dropped -- its block-size math was hand-tuned for the previous
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# model's dense 64-layer/8-head attention and doesn't carry over to
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# GDN's hybrid KV structure. Re-add once real numbers are known.
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- --model=Qwen/Qwen3.5-35B-A3B-GPTQ-Int4
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- --served-model-name=reasoning
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- --quantization=moe_wna16
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- --dtype=float16
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- --kv-cache-dtype=auto
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- --tensor-parallel-size=2
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- --max-model-len=16384
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- --gpu-memory-utilization=0.5
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- --max-num-seqs=4
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- --enable-chunked-prefill
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- --enable-prefix-caching
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# qwen3 is vLLM's dedicated reasoning parser for this family's <think>
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# blocks, also documented for Qwen3.5.
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- --reasoning-parser=qwen3
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# qwen3_coder is the checkpoint README's documented tool-call parser
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# for this specific GPTQ-Int4 release -- not hermes.
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- --enable-auto-tool-choice
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- --tool-call-parser=qwen3_coder
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# skip CUDA graph capture / torch.compile -- this arch's custom ops
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# (mamba_mixer2, gdn_attention_core) are compiling for the first time
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# ever on this hardware with no cache, and startupProbe kept killing
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# the pod mid-compile every ~20min before it could finish. Trade some
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# runtime throughput for a startup that actually completes; revisit
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# once this is confirmed working end to end.
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- --enforce-eager
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- --host=0.0.0.0
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- --port=8080
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env:
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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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image: vllm/vllm-openai:v0.17.0@sha256:2296a2a7e1ce1dc59c6577ba5900f4e9910b76c4a0cb134833a8137f92404dfa
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name: kserve-container
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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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resources:
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limits:
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cpu: '16'
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memory: 36Gi
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nvidia.com/gpu: '2'
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requests:
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cpu: '8'
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memory: 12Gi
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nvidia.com/gpu: '2'
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startupProbe:
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failureThreshold: 240
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httpGet:
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path: /health
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port: 8080
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periodSeconds: 15
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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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deploymentStrategy:
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type: Recreate
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maxReplicas: 1
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minReplicas: 1
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nodeSelector:
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kubernetes.io/hostname: worker-1
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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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- emptyDir:
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medium: Memory
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sizeLimit: 2Gi
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name: shm
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