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vLLM serving, production shape

Context: on-prem LLM inference — the kind where the data cannot leave. Two serving profiles: an 8-GPU node running GLM-5.2/5.3 (NVFP4 MoE, expert parallel, 256K context), and a single L40S running gemma-4-26B FP8. Ubuntu 24.04, vLLM in a uv-managed venv, systemd on top. The front door in front of these engines — keys, routing, TLS — is the LiteLLM proxy entry. Hostnames genericized; the API key below is a placeholder.

Install

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install vllm --torch-backend=auto

The unglamorous apt layer underneath: ffmpeg (torchcodec wants it), numactl (NUMA binding), build-essential + python3.12-dev (Triton compiles), ninja-build.

Models

pip install -U huggingface_hub
hf download nvidia/GLM-5.2-NVFP4 --local-dir /models/GLM-5.2-NVFP4
hf download google/gemma-4-26B-A4B-it --local-dir /models/gemma-4-26B-A4B-it
hf download google/gemma-4-26B-A4B-it-assistant ...   # MTP draft model
hf download Qwen/Qwen2.5-VL-72B-Instruct-AWQ ...      # vision

What bit

  1. trtllm_batch_decode_with_kv_cache_mla() got an unexpected keyword argument 'kv_scale_format' — the pinned FlashInfer is older than the vLLM calling it. Upgrade FlashInfer and tell vLLM to stop checking versions:

    pip install -U flashinfer-python flashinfer-cubin
export FLASHINFER_DISABLE_VERSION_CHECK=1

  2. Half finished FlashInferMLASparseSM120Impl — a literally half-finished backend class for the SM120 GPU. Patch it: back up the file, apply the patch script, restart vLLM.

  3. The CUDA graph memory hint nobody reads. At --gpu-memory-utilization 0.95, vLLM logs that with CUDA graph memory profiling this is effectively 0.9124 — and suggests 0.9876 for the same usable KV cache. Tempting; 0.98 OOMs at 256K context on this hardware. 0.95 with --max-model-len 256K is the stable point.

  4. Model upgrades pull vLLM upgrades. GLM-5.3-NVFP4 needed vLLM 0.28.0; the node ran 0.25.0. Back up the whole venv first (cp -a .venv .venv-vllm025-backup), then:

    pip install -U "vllm>=0.28.0" --extra-index-url https://flashinfer.ai/whl
pip install -U flashinfer-cubin==0.6.16.post3 --extra-index-url https://flashinfer.ai/whl

  5. MTP on gemma needed a patch to vllm/v1/spec_decode/llm_base_proposer.py — the draft model's embedding path. Patch script writes its own .bak; restart vLLM after.

The serving commands

8-GPU node, the final stable shape:

vllm serve /models/GLM-5.2-NVFP4 --served-model-name glm-5.2 \
  --tensor-parallel-size 8 --enable-expert-parallel \
  --disable-custom-all-reduce --numa-bind --performance-mode interactivity \
  --trust-remote-code --kv-cache-dtype fp8_e4m3 \
  --gpu-memory-utilization 0.95 --max-model-len 256K \
  --host <ip> --port 8000

Single L40S, FP8, with MTP speculative decoding from the draft model:

vllm serve /models/gemma-4-26B-A4B-it --quantization fp8 \
  --served-model-name gemma-4-26b-a4b --tensor-parallel-size 1 \
  --gpu-memory-utilization 0.93 --kv-cache-dtype fp8 --max-model-len 32768 \
  --max-num-seqs 64 --enable-prefix-caching \
  --cudagraph-capture-sizes 1 2 4 8 16 --async-scheduling \
  --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4 \
  --speculative-config '{"method":"mtp","model":"/models/gemma-4-26B-A4B-it-assistant","num_speculative_tokens":4}' \
  --trust-remote-code --host <ip> --port 8000

Production shape

Cache directories before the first start — vLLM, Triton, torchinductor, CUDA and huggingface each want one:

mkdir /models /var/cache/huggingface /var/cache/vllm /var/cache/triton \
      /var/cache/torchinductor /var/cache/nv

Environment in /etc/vllm/vllm.env (chmod 400), the essentials:

VLLM_API_KEY=<key>
HF_HOME=/var/cache/huggingface
VLLM_CACHE_ROOT=/var/cache/vllm
TRITON_CACHE_DIR=/var/cache/triton
TORCHINDUCTOR_CACHE_DIR=/var/cache/torchinductor
CUDA_MODULE_LOADING=LAZY
CUDA_DEVICE_ORDER=PCI_BUS_ID
NCCL_CUMEM_ENABLE=1
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
FLASHINFER_DISABLE_VERSION_CHECK=1
HF_HUB_OFFLINE=1        # model is on disk; do not phone home

The systemd unit earns its keep on the restart and shutdown behavior — a 400K-context model takes minutes to load and seconds to corrupt if killed wrong:

[Service]
Type=simple
EnvironmentFile=/etc/vllm/vllm.env
ExecStart=/root/.venv/bin/vllm serve ...
Restart=on-failure
RestartSec=10
TimeoutStartSec=900
TimeoutStopSec=120
KillSignal=SIGINT
KillMode=mixed
LimitNOFILE=1048576
OOMScoreAdjust=-900

Front it with nftables — default drop input, allow established, loopback, ICMP both families, SSH, and the API ports only:

table inet filter {
    chain input {
        type filter hook input priority filter; policy drop;
        ct state established,related accept
        ct state invalid drop
        iif lo accept
        ip protocol icmp accept
        ip6 nexthdr icmpv6 accept
        tcp dport 22 accept
        tcp dport 8000-8001 accept
    }
}

For benchmarking, ShareGPT (Vicuna unfiltered split) through the OpenAI-compatible API with a small concurrency harness — numbers before and after every flag change, or the flag changes are just feelings.

This saved you a night? I do this for a living: info@wirt.ee.