nvidia-model-optimizer-deployment

name	nvidia-model-optimizer-deployment
description	Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).
license	Apache-2.0
metadata	{"author":"NVIDIA"}

Deployment Skill

Serve a model checkpoint as an OpenAI-compatible inference endpoint. Supports vLLM, SGLang, and TRT-LLM (including AutoDeploy).

Quick Start

Prefer scripts/deploy.sh for standard local deployments — it handles quant detection, health checks, and server lifecycle. Use the raw framework commands in Step 4 when you need flags the script doesn't support, or for remote deployment.

# Start vLLM server with a ModelOpt checkpoint
scripts/deploy.sh start --model ./qwen3-0.6b-fp8

# Start with SGLang and tensor parallelism
scripts/deploy.sh start --model ./llama-70b-nvfp4 --framework sglang --tp 4

# Start from HuggingFace hub
scripts/deploy.sh start --model nvidia/Llama-3.1-8B-Instruct-FP8

# Test the API
scripts/deploy.sh test

# Check status
scripts/deploy.sh status

# Stop
scripts/deploy.sh stop

The script handles: GPU detection, quantization flag auto-detection (FP8 vs FP4), server lifecycle (start/stop/restart/status), health check polling, and API testing.

Decision Flow

0. Check workspace (multi-user / Slack bot)

If MODELOPT_WORKSPACE_ROOT is set, read common/workspace-management.md. Before creating a new workspace, check for existing ones — especially if deploying a checkpoint from a prior PTQ run:

ls "$MODELOPT_WORKSPACE_ROOT/" 2>/dev/null

If the user says "deploy the model I just quantized" or references a previous PTQ, find the matching workspace and cd into it. The checkpoint should be in that workspace's output directory.

1. Identify the checkpoint

Determine what the user wants to deploy:

• Local quantized checkpoint (from ptq skill or manual export): look for hf_quant_config.json in the directory. If coming from a prior PTQ run in the same workspace, check common output locations: output/, outputs/, exported_model/, or the --export_path used in the PTQ command.
• HuggingFace model hub (e.g., nvidia/Llama-3.1-8B-Instruct-FP8): use directly
• Unquantized model: deploy as-is (BF16) or suggest quantizing first with the ptq skill

Note: This skill expects HF-format checkpoints (from PTQ with --export_fmt hf). TRT-LLM format checkpoints should be deployed directly with TRT-LLM — see references/trtllm.md.

Check the quantization format if applicable:

cat <checkpoint_path>/hf_quant_config.json 2>/dev/null || echo "No hf_quant_config.json"

If not found, also check config.json for a quantization_config section with quant_method: "modelopt". If neither exists, the checkpoint is unquantized.

2. Choose the framework

If the user hasn't specified a framework, recommend based on this priority:

Situation	Recommended	Why
General use	vLLM	Widest ecosystem, easy setup, OpenAI-compatible
Best SGLang model support	SGLang	Strong DeepSeek/Llama 4 support
Maximum optimization	TRT-LLM	Best throughput via engine compilation
Mixed-precision / AutoQuant	TRT-LLM AutoDeploy	Only option for AutoQuant checkpoints

Check the support matrix in references/support-matrix.md to confirm the model + format + framework combination is supported.

3. Check the environment

Read common/environment-setup.md for GPU detection, local vs remote, and SLURM/Docker/bare metal detection. After completing it you should know: GPU model/count, local or remote, and execution environment.

Then check the deployment framework is installed:

python -c "import vllm; print(f'vLLM {vllm.__version__}')" 2>/dev/null || echo "vLLM not installed"
python -c "import sglang; print(f'SGLang {sglang.__version__}')" 2>/dev/null || echo "SGLang not installed"
python -c "import tensorrt_llm; print(f'TRT-LLM {tensorrt_llm.__version__}')" 2>/dev/null || echo "TRT-LLM not installed"

If not installed, consult references/setup.md.

GPU memory estimate (to determine tensor parallelism):

• BF16: params × 2 bytes (8B ≈ 16 GB)
• FP8: params × 1 byte (8B ≈ 8 GB)
• FP4: params × 0.5 bytes (8B ≈ 4 GB)
• Add ~2-4 GB for KV cache and framework overhead

If the model exceeds single GPU memory, use tensor parallelism (-tp <num_gpus>).

4. Deploy

Read the framework-specific reference for detailed instructions:

Framework	Reference file
vLLM	references/vllm.md
SGLang	references/sglang.md
TRT-LLM	references/trtllm.md

Quick-start commands (for common cases):

vLLM

# Serve as OpenAI-compatible endpoint
python -m vllm.entrypoints.openai.api_server \
    --model <checkpoint_path> \
    --quantization modelopt \
    --tensor-parallel-size <num_gpus> \
    --host 0.0.0.0 --port 8000

For NVFP4 checkpoints, use --quantization modelopt_fp4.

SGLang

python -m sglang.launch_server \
    --model-path <checkpoint_path> \
    --quantization modelopt \
    --tp <num_gpus> \
    --host 0.0.0.0 --port 8000

TRT-LLM (direct)

from tensorrt_llm import LLM, SamplingParams
llm = LLM(model="<checkpoint_path>")
outputs = llm.generate(["Hello, my name is"], SamplingParams(temperature=0.8, top_p=0.95))

TRT-LLM AutoDeploy

For AutoQuant or mixed-precision checkpoints, see references/trtllm.md.

5. Verify the deployment

After the server starts, verify it's healthy:

# Health check
curl -s http://localhost:8000/health

# List models
curl -s http://localhost:8000/v1/models | python -m json.tool

# Test generation
curl -s http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "<model_name>",
        "prompt": "The capital of France is",
        "max_tokens": 32
    }' | python -m json.tool

All checks must pass before reporting success to the user.

6. Remote deployment (SSH/SLURM)

If a cluster config exists (~/.config/modelopt/clusters.yaml or .claude/clusters.yaml), or the user mentions running on a remote machine:

0. Check container registry auth — before submitting any SLURM job with a container image, verify credentials exist on the cluster per common/slurm-setup.md section 6. If credentials are missing for the image's registry, ask the user to fix auth or switch to an image on an authenticated registry (e.g., NGC). Do not submit until auth is confirmed.

1. Source remote utilities:

   source .claude/common/remote_exec.sh
   remote_load_cluster
   remote_check_ssh
   remote_detect_env
   

2. Sync the checkpoint (only if it was produced locally):

   If the checkpoint path is a remote/absolute path (e.g., from a prior PTQ run on the cluster), skip sync — it's already there. Verify with remote_run "ls <checkpoint_path>/config.json". Only sync if the checkpoint is local:

   remote_sync_to <local_checkpoint_path> checkpoints/
   

3. Deploy based on remote environment:

   • SLURM — see common/slurm-setup.md for job script templates (container setup, account/partition discovery). The server command inside the container is the same as Step 4 (e.g., python -m vllm.entrypoints.openai.api_server --model <path> --quantization modelopt). After submitting, register the job and set up monitoring per the monitor skill. Get the node hostname from squeue -j $JOBID -o %N.

   • Bare metal / Docker — use remote_run to start the server directly:

     remote_run "nohup python -m vllm.entrypoints.openai.api_server --model <path> --port 8000 > deploy.log 2>&1 &"
     

4. Verify remotely:

   remote_run "curl -s http://localhost:8000/health"
   remote_run "curl -s http://localhost:8000/v1/models"
   

5. Report the endpoint — include the remote hostname and port so the user can connect (e.g., http://<node_hostname>:8000). For SLURM, note that the port is only reachable from within the cluster network.

For NEL-managed deployment (evaluation with self-deployment), use the evaluation skill instead — NEL handles SLURM container deployment, health checks, and teardown automatically.

Error Handling

Error	Cause	Fix
CUDA out of memory	Model too large for GPU(s)	Increase --tensor-parallel-size or use a smaller model
quantization="modelopt" not recognized	vLLM/SGLang version too old	Upgrade: vLLM >= 0.10.1, SGLang >= 0.4.10
hf_quant_config.json not found	Not a ModelOpt-exported checkpoint	Re-export with export_hf_checkpoint(), or remove --quantization flag
Connection refused on health check	Server still starting	Wait 30-60s for large models; check logs for errors
modelopt_fp4 not supported	Framework doesn't support FP4 for this model	Check support matrix in references/support-matrix.md

Unsupported Models

If the model is not in the validated support matrix (references/support-matrix.md), deployment may fail due to weight key mismatches, missing architecture mappings, or quantized/unquantized layer confusion. Read references/unsupported-models.md for the iterative debug loop: run → read error → diagnose → patch framework source → re-run. For kernel-level issues, escalate to the framework team rather than attempting fixes.

Success Criteria

1. Server process is running and healthy (/health returns 200)
2. Model is listed at /v1/models
3. Test generation produces coherent output
4. Server URL and port are reported to the user
5. If benchmarking was requested, throughput/latency numbers are reported