121 lines
4.7 KiB
YAML
121 lines
4.7 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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# DeepSeek-R1-Distill-32B retired: tool_choice="auto" (what pi sends)
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# hit a documented vLLM/R1-family conflict -- the model narrated fake
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# tool_calls in its <think> block instead of emitting real ones,
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# regardless of parser. Tried swapping to a Kimi-distilled Qwen3.6
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# MoE checkpoint and an AWQ-quantized Qwen3-30B-A3B first -- both
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# failed on real, separate blockers (unrecognized model_type; then
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# marlin INT4 kernels needing compute capability 80+, but worker-1's
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# GPU is sm70/V100). Landed on dense Qwen3-32B instead: native Qwen3
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# tool-call format (no narration bug), bnb-4bit works fine on sm70
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# (proven by the old DeepSeek config already), and no MoE
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# arch/quantization risk this time.
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- --model=unsloth/Qwen3-32B-bnb-4bit
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- --served-model-name=reasoning
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- --quantization=bitsandbytes
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- --dtype=float16
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- --kv-cache-dtype=auto
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- --tensor-parallel-size=1
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- --pipeline-parallel-size=2
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- --max-model-len=16384
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- --gpu-memory-utilization=0.90
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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.
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- --reasoning-parser=qwen3
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# hermes is the documented tool-call parser for general (non-Coder)
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# Qwen3 models -- native chat template support, not narrated text.
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- --enable-auto-tool-choice
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- --tool-call-parser=hermes
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# vLLM 0.11.0's native OffloadingConnector -- spills KV cache blocks
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# to CPU DRAM instead of discarding them on preemption (max-num-seqs=4
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# + max-model-len=16384 means concurrent long sequences compete for
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# the same GPU KV space). No extra dependency, built into vLLM core.
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# num_cpu_blocks=2000 hung the pod at startup on the old model (2000 x
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# ~32MB/block blew well past the pod's memory limit). num_cpu_blocks=32
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# was the safe-recovery value after that -- only ~1GB of real DRAM
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# (32 blocks x 128 tokens x 256KB/token-across-all-64-layers, fp16),
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# basically a token-count safety valve, not real offload capacity.
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# This model: 64 layers, 8 KV heads x 128 head_dim, fp16 -> ~256KB of
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# KV per token across all layers -> ~32MB per 128-token block.
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# num_cpu_blocks=256 -> ~8GB of actual DRAM offload (32,768 tokens),
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# comfortably under the pod's 36Gi limit alongside the ~20GB bnb-4bit
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# weights. Watch real host memory on boot before raising further --
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# block_size=128 tokens matches vLLM's own example.
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# Note: 0.11.0 ships the original (fragmented, small-transfer-block)
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# version of this connector -- 0.12.0 consolidates KV data into one
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# contiguous block per request and is reported an order of magnitude
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# faster for this specific feature, so this is a real but not yet
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# optimal implementation until the image gets bumped.
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- --kv-transfer-config={"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"num_cpu_blocks":256,"block_size":128}}
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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:v0.11.0@sha256:014a95f21c9edf6abe0aea6b07353f96baa4ec291c427bb1176dc7c93a85845c
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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: 80
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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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