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Layout on 4x Tesla V100 32GB (PCIe, no NVLink), all TP=1: GPU0+1 vLLM DeepSeek-R1-Distill-Qwen-32B bnb-nf4 (2 replicas) GPU2 Ollama ornith:35b + qwen2.5:3b-instruct (co-resident) GPU3 vLLM Qwen2.5-Math-PRM-7B (reward model) CPU TEI nomic-embed-text-v2-moe, bge-reranker-base Volta constraints, each verified against live output rather than config: - vLLM pinned v0.11.0: sm_70 dropped from CUDA_SUPPORTED_ARCHS at v0.11.1. - AWQ hard-rejected (needs sm_75). GPTQ passes vLLM's min_capability=60 gate but is NUMERICALLY WRONG on sm_70 — emits garbage logits. Proven by an fp16 control run producing correct text on an identical backend. bitsandbytes nf4 verified correct by output. - flashinfer's check_cuda_arch() crashes on any sm_7x (calls .isdigit() on an int) -> VLLM_USE_FLASHINFER_SAMPLER=0. - xformers has no sm_70 kernel for V1's paged-attention bias, and V0 was removed in v0.11.0 -> TRITON_ATTN. - Ornith is Qwen3.5-MoE hybrid-attention; vLLM added that arch after dropping Volta, so no build has both -> Ollama, which also multiplexes a second model on the same card for free. Cluster prereqs that were absent: - RuntimeClass nvidia: the Talos toolkit extension registers the containerd handler but not the k8s object; without it every pod is rejected at admission. - gpu-system pinned to privileged PSA: a device plugin cannot satisfy the cluster-default baseline, it must mount hostPath. - device-plugin affinity=null: the chart requires NFD labels that do not exist here, so it matched zero nodes and reported desiredNumberScheduled=0 silently. - Recreate strategy on GPU services: with GPUs allocated exactly 4/4, a RollingUpdate surge pod has no card and deadlocks the rollout. - longhorn-llm-local SC (1 replica, strict-local, disk tag llm): the default 3-replica class could not place the volume at all (every control-plane disk was at its over-provisioning ceiling), and this keeps ~60GB of weights on worker-1's own NVMe instead of reading them over the network. deploy-gpu-serving.sh sequences ArgoCD syncs (or helm/kubectl in --manual mode) and never applies a manifest absent from git; doctor/unstick/teardown stages exist so this is diagnosable without ad-hoc kubectl archaeology.
134 lines
5.9 KiB
YAML
134 lines
5.9 KiB
YAML
# Reasoning engine — DeepSeek-R1-Distill-Qwen-32B, GPTQ INT4, vLLM.
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#
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# TP=1 with 2 data-parallel replicas (GPU0 + GPU1) rather than one TP=2 engine:
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# worker-1 has NO NVLink, so tensor-parallel's per-token all-reduce would cross
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# PCIe on every decode step. Two independent replicas need zero inter-GPU
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# communication and KServe load-balances them behind one Service.
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#
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# vLLM is pinned to v0.11.0 — the LAST release that compiles sm_70 (Volta)
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# kernels. v0.11.1 dropped 7.0 from CUDA_SUPPORTED_ARCHS. Do not bump this
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# without re-checking CMakeLists.txt, or every pod dies with "no kernel image".
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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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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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spec:
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predictor:
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minReplicas: 2
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# Recreate, not the default RollingUpdate: GPUs are allocated exactly 4/4,
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# so a surge pod has no card to claim and sits Pending while the old pod is
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# never torn down — a deadlock. Recreate tears down first, accepting a brief
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# gap during updates.
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deploymentStrategy:
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type: Recreate
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maxReplicas: 2
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nodeSelector:
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kubernetes.io/hostname: worker-1
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runtimeClassName: nvidia
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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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# GPTQ, NOT AWQ. vLLM hard-refuses AWQ below compute capability 75:
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# "The quantization method awq is not supported for the current GPU.
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# Minimum capability: 75. Current capability: 70."
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# V100 is sm_70. GPTQ's min capability is 60, so it runs. (gptq_marlin
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# needs 80 and fp8 needs 80 — both also out.) Same 19.3GB footprint.
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# desc_act=False in this build: no activation reordering, faster.
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- --model=unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit
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- --served-model-name=reasoning
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# bitsandbytes nf4. GPTQ passed vLLM's min_capability=60 check but was
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# numerically WRONG on sm_70 (garbage logits) — proven by the fp16
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# control run producing correct text with the identical backend. bnb
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# declares min_capability=70, but treat that as unverified until the
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# output itself is checked.
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# NOTE the repo sets bnb_4bit_compute_dtype=bfloat16, which Volta does
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# not have; --dtype=float16 must override it.
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- --quantization=bitsandbytes
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# Volta has no bf16 — must be explicit, the repo's weights are bf16.
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- --dtype=float16
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# No FP8 KV on Volta; stays fp16.
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- --kv-cache-dtype=auto
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- --tensor-parallel-size=1
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- --max-model-len=16384
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# VRAM budget on a 32GiB V100: 0.92 => ~29.4GiB, minus ~18GiB of GPTQ
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# weights leaves ~11GiB for KV + activations. One full 32K sequence
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# costs 32768 x 256KB = 8GiB of KV, so 8 concurrent full-length
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# sequences is not physically possible here — 4 is honest, and a
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# serial single-user harness never needs more.
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- --gpu-memory-utilization=0.90
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- --max-num-seqs=4
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# Smooths Volta's slow prefill (no FlashAttention2 on sm_70).
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- --enable-chunked-prefill
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- --enable-prefix-caching
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# Splits <think>…</think> into its own channel.
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- --reasoning-parser=deepseek_r1
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- --host=0.0.0.0
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- --port=8080
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env:
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# FlashAttention2 requires sm_80; Volta must fall back to xformers.
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# flashinfer's check_cuda_arch() has an upstream bug that crashes on
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# ANY sm_7x GPU: `elif major == 7 and minor.isdigit()` calls .isdigit()
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# on an int, so instead of reporting "unsupported" it raises
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# AttributeError: 'int' object has no attribute 'isdigit'
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# and engine init dies. Default is None (auto-detect), which walks
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# straight into that path. 0 disables the flashinfer sampler outright.
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# Only affects the generate runner — the verifier (pooling) never hits
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# the sampler, which is why it started fine and this did not.
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- name: VLLM_USE_FLASHINFER_SAMPLER
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value: "0"
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# TRITON_ATTN, not XFORMERS. On sm_70 every xformers kernel is
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# rejected for V1's paged-attention bias type:
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# fa2F / triton_splitKF -> require sm_80
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# cutlassF -> supports sm_70 but not
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# PagedBlockDiagonalCausalWithOffsetPaddedKeysMask
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# -> NotImplementedError kills EngineCore on the FIRST request, which
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# takes the whole pod down (vLLM treats engine death as fatal).
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# V0, whose hand-written paged kernels did support sm_70, was REMOVED
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# in v0.11.0, so VLLM_USE_V1=0 has nothing to fall back to.
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# Triton JIT-compiles for the local arch, so it is the last option.
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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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resources:
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requests:
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cpu: "8"
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memory: 8Gi
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nvidia.com/gpu: "1"
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limits:
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cpu: "16"
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memory: 16Gi
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nvidia.com/gpu: "1"
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volumeMounts:
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- name: models
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mountPath: /mnt/models
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- name: shm
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mountPath: /dev/shm
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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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# Cold start pulls ~18Gi of weights over Longhorn, then loads to VRAM.
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periodSeconds: 15
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failureThreshold: 80
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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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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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