RickyDeSkywalker/vllm-container

vLLM Container for DeltaAI (aarch64 / GH200)

Self-contained Apptainer SIF for running vLLM on NVIDIA GH200 (Grace-Hopper, aarch64) nodes. Baked with vLLM bleeding-edge, Transformers 5.x (for new models like gemma-4), and all runtime deps. No build-time setup needed on the target machine.

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1. Contents

Component	Version
Base image	NGC nvcr.io/nvidia/pytorch:26.03-py3
CUDA runtime	13.2 (with cuda-compat 595.45.04 baked in for older drivers)
PyTorch	2.11.0a0 (NGC build)
Triton	3.6.0 (NGC build)
flash_attn	2.7.4.post1 (NGC build)
vLLM	0.19.1rc1.dev285+g19ec9a0a6
Transformers	5.5.4 (installed into /opt/extra_pkgs, auto-loaded via PYTHONPATH)
huggingface_hub	0.36.2
Tool parsers registered	34 (includes qwen3_coder, gemma4, deepseek_v3, ...)

Note: vLLM's internal metadata says it wants transformers<5. We bypass that at runtime by shadowing the system Transformers 4.57 with 5.5.4 from /opt/extra_pkgs via PYTHONPATH (set up automatically by /.singularity.d/env/92-extra-packages.sh).

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2. Hardware / software requirements

Requirement	Value
CPU arch	aarch64 (ARM64 — Grace, Ampere Altra, Neoverse)
GPU	NVIDIA Hopper-class (GH200, H100, H200). Compute capability ≥ 9.0 assumed.
NVIDIA driver	R535 or newer (compat layer in SIF handles up to CUDA 13.2). DeltaAI's 570.172.08 is compatible.
Apptainer	1.3+ (tested on 1.4.2). Singularity CE 4.x should also work.
Disk for SIF	~40 GB
GPU memory	80 GB+ per GPU recommended for TP=1 on 27-30B models

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3. Quick start

SIF=/path/to/vllm.sif

apptainer run --nv $SIF python -c "import vllm; print(vllm.__version__)"

If that prints a version, you're done with setup. Proceed to section 4 for a real serve command.

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4. Required bind mounts and env vars

The SIF is read-only. To let vLLM write caches and find your model checkpoints, you must provide writable host paths via --bind and point env vars at them.

4.1 Minimal set

# HuggingFace model cache (the model weights live here)
--bind /path/on/host/.cache/huggingface:/hf_cache
--env HF_HOME=/hf_cache
--env HF_HUB_CACHE=/hf_cache/hub
--env HF_HUB_DISABLE_IMPLICIT_TOKEN=1

# Runtime caches (torch.compile, triton JIT, vLLM model info, etc.)
--bind /path/on/host/cache_dir:/app_cache
--env XDG_CACHE_HOME=/app_cache
--env VLLM_CACHE_ROOT=/app_cache/vllm
--env TRITON_CACHE_DIR=/app_cache/triton
--env TORCHINDUCTOR_CACHE_DIR=/app_cache/inductor

Create the cache dirs once: mkdir -p $HOME/vllm_cache/{vllm,triton,inductor}.

4.2 Why this is needed

• Apptainer's auto-home mount breaks on directories protected by POSIX ACLs (common on HPC cluster home dirs), so we bind our own paths explicitly.
• vLLM writes: ~/.cache/vllm/modelinfos, torch.compile cache, triton JIT cache. All of these need a writable persistent path.
• HF_HUB_DISABLE_IMPLICIT_TOKEN=1 avoids the container trying to read a non-existent token file when home has no HF credentials.

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5. Example: Serve Qwen3.5-27B with tool-calling

SIF=/work/nvme/bdjz/rwang18/vllm_container/vllm.sif
HF_CACHE=$HOME/.cache/huggingface
APP_CACHE=$HOME/vllm_cache
mkdir -p $APP_CACHE

apptainer run --nv \
    --bind $HF_CACHE:/hf_cache \
    --bind $APP_CACHE:/app_cache \
    --env HF_HOME=/hf_cache \
    --env HF_HUB_CACHE=/hf_cache/hub \
    --env HF_HUB_DISABLE_IMPLICIT_TOKEN=1 \
    --env XDG_CACHE_HOME=/app_cache \
    --env VLLM_CACHE_ROOT=/app_cache/vllm \
    --env TRITON_CACHE_DIR=/app_cache/triton \
    --env TORCHINDUCTOR_CACHE_DIR=/app_cache/inductor \
    $SIF \
    python -m vllm.entrypoints.openai.api_server \
    --model Qwen/Qwen3.5-27B \
    --port 8000 \
    --data-parallel-size 4 \
    --max-model-len 163840 \
    --quantization fp8 \
    --gdn-prefill-backend triton \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder

Flag reference

Flag	Why
--nv	Inject host NVIDIA driver libs
--data-parallel-size 4	One vLLM engine per GPU (4x GH200)
--quantization fp8	GH200 native FP8; halves weight memory, frees mamba cache blocks
--gdn-prefill-backend triton	Avoid missing flashinfer dependency for Qwen3.5 linear attention
--reasoning-parser qwen3	Parses <thinking>...</thinking> from Qwen3 output
--tool-call-parser qwen3_coder	Required for tool/function calling with Qwen3.5-Coder

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6. Example: Serve a model that needs Transformers 5.x (e.g. gemma-4)

Exactly the same command as above — Transformers 5.5.4 is preloaded into the container. Just swap the model name and parser:

apptainer run --nv \
    [... same bind/env flags as above ...] \
    $SIF \
    python -m vllm.entrypoints.openai.api_server \
    --model google/gemma-4-31B-it \
    --port 8000 \
    --data-parallel-size 4 \
    --max-model-len 262144 \
    --quantization fp8 \
    --enable-auto-tool-choice \
    --tool-call-parser gemma4

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7. Querying the served endpoint

vLLM defaults to 0.0.0.0:8000, so any machine that can reach the compute node can hit it.

# From the same node:
curl http://localhost:8000/v1/models

# From another node on the cluster:
curl http://<node_hostname>:8000/v1/models

# From outside the cluster (e.g. your laptop):
ssh -L 8000:<node_hostname>:8000 user@login_host
# then in your laptop browser: http://localhost:8000

OpenAI-compatible chat completion:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Qwen/Qwen3.5-27B",
        "messages": [{"role": "user", "content": "hello"}]
    }'

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8. Tunable knobs for resource-constrained setups

Symptom	Fix
max_num_seqs (1024) exceeds available Mamba cache blocks (N)	Add --quantization fp8 (frees memory), OR --max-num-seqs <N>, OR --gpu-memory-utilization 0.95
Model too large to fit one GPU	Use --tensor-parallel-size 4 (shards weights across 4 GPUs) instead of --data-parallel-size 4
Context length too short	Increase --max-model-len; you may need to lower --max-num-seqs to compensate
First run very slow	JIT compiling kernels; cache is persisted in $APP_CACHE/triton + $APP_CACHE/inductor, so second run is fast

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9. Troubleshooting

cuDriverGetVersion = 12080 instead of 13020

The cuda-compat layer isn't active. Sanity-check:

apptainer exec --nv $SIF bash -c 'echo $LD_LIBRARY_PATH; python -c "import ctypes; l=ctypes.CDLL(\"libcuda.so.1\"); v=ctypes.c_int(); l.cuDriverGetVersion(ctypes.byref(v)); print(v.value)"'

Expected: /usr/local/cuda/compat as the first entry in LD_LIBRARY_PATH, and cuDriverGetVersion = 13020. If missing, the env-script /.singularity.d/env/91-cuda-compat.sh didn't fire — check apptainer --version is ≥ 1.3.

Permission denied on ~/.cache/huggingface/...

Your cluster's home dir probably uses POSIX ACLs that don't survive user namespace. Use --bind /explicit/path/to/cache:/hf_cache (section 4) rather than relying on auto-home.

ModuleNotFoundError: No module named 'flashinfer'

Some models (linear-attention Qwen3.5) try to use flashinfer for prefill. It's not installed in this SIF. Pass --gdn-prefill-backend triton to fall back to the Triton kernel (~5-10% slower but works).

NVIDIA Driver Release 595.45 or later ... compatibility mode is UNAVAILABLE

This NGC entrypoint warning is a false positive — it doesn't detect our baked-in cuda-compat. The actual runtime verifies with cuDriverGetVersion = 13020 (see above). Safe to ignore.

Model weights not found / re-downloading

vLLM looks in $HF_HUB_CACHE (which you set to /hf_cache/hub via --env). Make sure the model is already in <host>/.cache/huggingface/hub/models--<org>--<name>/ and that you bind-mounted the correct parent directory.

Container crashes silently right after launch

Check nvidia-smi --query-compute-apps=pid --format=csv — a previous vLLM run may have orphaned workers holding the GPU. Clean up:

pkill -9 -u $USER -f "VLLM::Worker\|vllm\|api_server"

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10. What's baked in vs. what's bound

At runtime the container sees a merged view:

• Read-only squashfs (ID 3 in apptainer sif list): the NGC 26.03 base.
• Read-only ext3 overlay (ID 4): cuda-compat, vLLM, tool parsers, /opt/extra_pkgs/ with Transformers 5.x.
• Your bind mounts: /hf_cache, /app_cache.
• Auto-injected by --nv: host NVIDIA driver libs (libcuda.so, libnvidia-ml.so, nvidia-smi).

Nothing inside the SIF can be modified at runtime. To upgrade any baked-in package, you'd need to rebuild the SIF (see repo-root install script).

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11. Moving the SIF between machines

The SIF is a single file, portable across aarch64 Linux boxes. Copy:

scp vllm.sif user@other_host:/path/
# or
rsync -avh --progress vllm.sif user@other_host:/path/

Target machine just needs:

• apptainer installed
• aarch64 + NVIDIA driver ≥ R535
• Enough disk for the SIF (~40 GB) and model checkpoints

No python, no conda, no vllm install on the target.

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12. Files in this directory

File	Purpose
vllm.sif	The actual container image
README.md	This file

For the build recipe (how vllm.sif was produced) see /taiga/illinois/eng/cs/tozhang/ricky/vllm_container/install_container.sh.