serving-llms-vllm

Q: How do I install serving-llms-vllm?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill serving-llms-vllm in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/serving-llms-vllm/ (personal) or .claude/skills/serving-llms-vllm/ (project).

by orchestra-researchAI & Agents

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/orchestra-research/ai-research-skills --skill serving-llms-vllm

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads serving-llms-vllm automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

vLLM - High-Performance LLM Serving

Quick start

vLLM achieves 24x higher throughput than standard transformers through PagedAttention (block-based KV cache) and continuous batching (mixing prefill/decode requests).

Installation:

pip install vllm

Basic offline inference:

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3-8B-Instruct")
sampling = SamplingParams(temperature=0.7, max_tokens=256)

outputs = llm.generate(["Explain quantum computing"], sampling)
print(outputs[0].outputs[0].text)

OpenAI-compatible server:

vllm serve meta-llama/Llama-3-8B-Instruct

# Query with OpenAI SDK
python -c "
from openai import OpenAI
client = OpenAI(base_url='http://localhost:8000/v1', api_key='EMPTY')
print(client.chat.completions.create(
    model='meta-llama/Llama-3-8B-Instruct',
    messages=[{'role': 'user', 'content': 'Hello!'}]
).choices[0].message.content)
"

Common workflows

Workflow 1: Production API deployment

Copy this checklist and track progress:

Deployment Progress:
- [ ] Step 1: Configure server settings
- [ ] Step 2: Test with limited traffic
- [ ] Step 3: Enable monitoring
- [ ] Step 4: Deploy to production
- [ ] Step 5: Verify performance metrics

Step 1: Configure server settings

Choose configuration based on your model size:

# For 7B-13B models on single GPU
vllm serve meta-llama/Llama-3-8B-Instruct \
  --gpu-memory-utilization 0.9 \
  --max-model-len 8192 \
  --port 8000

# For 30B-70B models with tensor parallelism
vllm serve meta-llama/Llama-2-70b-hf \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.9 \
  --quantization awq \
  --port 8000

# For production with caching and metrics
vllm serve meta-llama/Llama-3-8B-Instruct \
  --gpu-memory-utilization 0.9 \
  --enable-prefix-caching \
  --enable-metrics \
  --metrics-port 9090 \
  --port 8000 \
  --host 0.0.0.0

Step 2: Test with limited traffic

Run load test before production:

# Install load testing tool
pip install locust

# Create test_load.py with sample requests
# Run: locust -f test_load.py --host http://localhost:8000

Verify TTFT (time to first token) < 500ms and throughput > 100 req/sec.

Step 3: Enable monitoring

vLLM exposes Prometheus metrics on port 9090:

curl http://localhost:9090/metrics | grep vllm

Key metrics to monitor:

vllm:time_to_first_token_seconds - Latency
vllm:num_requests_running - Active requests
vllm:gpu_cache_usage_perc - KV cache utilization

Step 4: Deploy to production

Use Docker for consistent deployment:

# Run vLLM in Docker
docker run --gpus all -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-3-8B-Instruct \
  --gpu-memory-utilization 0.9 \
  --enable-prefix-caching

Step 5: Verify performance metrics

Check that deployment meets targets:

TTFT < 500ms (for short prompts)
Throughput > target req/sec
GPU utilization > 80%
No OOM errors in logs

Workflow 2: Offline batch inference

For processing large datasets without server overhead.

Copy this checklist:

Batch Processing:
- [ ] Step 1: Prepare input data
- [ ] Step 2: Configure LLM engine
- [ ] Step 3: Run batch inference
- [ ] Step 4: Process results

Step 1: Prepare input data

# Load prompts from file
prompts = []
with open("prompts.txt") as f:
    prompts = [line.strip() for line in f]

print(f"Loaded {len(prompts)} prompts")

Step 2: Configure LLM engine

from vllm import LLM, SamplingParams

llm = LLM(
    model="meta-llama/Llama-3-8B-Instruct",
    tensor_parallel_size=2,  # Use 2 GPUs
    gpu_memory_utilization=0.9,
    max_model_len=4096
)

sampling = SamplingParams(
    temperature=0.7,
    top_p=0.95,
    max_tokens=512,
    stop=["</s>", "\n\n"]
)

Step 3: Run batch inference

vLLM automatically batches requests for efficiency:

# Process all prompts in one call
outputs = llm.generate(prompts, sampling)

# vLLM handles batching internally
# No need to manually chunk prompts

Step 4: Process results

# Extract generated text
results = []
for output in outputs:
    prompt = output.prompt
    generated = output.outputs[0].text
    results.append({
        "prompt": prompt,
        "generated": generated,
        "tokens": len(output.outputs[0].token_ids)
    })

# Save to file
import json
with open("results.jsonl", "w") as f:
    for result in results:
        f.write(json.dumps(result) + "\n")

print(f"Processed {len(results)} prompts")

Workflow 3: Quantized model serving

Fit large models in limited GPU memory.

Quantization Setup:
- [ ] Step 1: Choose quantization method
- [ ] Step 2: Find or create quantized model
- [ ] Step 3: Launch with quantization flag
- [ ] Step 4: Verify accuracy

Step 1: Choose quantization method

AWQ: Best for 70B models, minimal accuracy loss
GPTQ: Wide model support, good compression
FP8: Fastest on H100 GPUs

Step 2: Find or create quantized model

Use pre-quantized models from HuggingFace:

# Search for AWQ models
# Example: TheBloke/Llama-2-70B-AWQ

Step 3: Launch with quantization flag

# Using pre-quantized model
vllm serve TheBloke/Llama-2-70B-AWQ \
  --quantization awq \
  --tensor-parallel-size 1 \
  --gpu-memory-utilization 0.95

# Results: 70B model in ~40GB VRAM

Step 4: Verify accuracy

Test outputs match expected quality:

# Compare quantized vs non-quantized responses
# Verify task-specific performance unchanged

When to use vs alternatives

Use vLLM when:

Deploying production LLM APIs (100+ req/sec)
Serving OpenAI-compatible endpoints
Limited GPU memory but need large models
Multi-user applications (chatbots, assistants)
Need low latency with high throughput

Use alternatives instead:

llama.cpp: CPU/edge inference, single-user
HuggingFace transformers: Research, prototyping, one-off generation
TensorRT-LLM: NVIDIA-only, need absolute maximum performance
Text-Generation-Inference: Already in HuggingFace ecosystem

Common issues

Issue: Out of memory during model loading

Reduce memory usage:

vllm serve MODEL \
  --gpu-memory-utilization 0.7 \
  --max-model-len 4096

Or use quantization:

vllm serve MODEL --quantization awq

Issue: Slow first token (TTFT > 1 second)

Enable prefix caching for repeated prompts:

vllm serve MODEL --enable-prefix-caching

For long prompts, enable chunked prefill:

vllm serve MODEL --enable-chunked-prefill

Issue: Model not found error

Use --trust-remote-code for custom models:

vllm serve MODEL --trust-remote-code

Issue: Low throughput (<50 req/sec)

Increase concurrent sequences:

vllm serve MODEL --max-num-seqs 512

Check GPU utilization with nvidia-smi - should be >80%.

Issue: Inference slower than expected

Verify tensor parallelism uses power of 2 GPUs:

vllm serve MODEL --tensor-parallel-size 4  # Not 3

Enable speculative decoding for faster generation:

vllm serve MODEL --speculative-model DRAFT_MODEL

Advanced topics

Server deployment patterns: See references/server-deployment.md for Docker, Kubernetes, and load balancing configurations.

Performance optimization: See references/optimization.md for PagedAttention tuning, continuous batching details, and benchmark results.

Quantization guide: See references/quantization.md for AWQ/GPTQ/FP8 setup, model preparation, and accuracy comparisons.

Troubleshooting: See references/troubleshooting.md for detailed error messages, debugging steps, and performance diagnostics.

Hardware requirements

Small models (7B-13B): 1x A10 (24GB) or A100 (40GB)
Medium models (30B-40B): 2x A100 (40GB) with tensor parallelism
Large models (70B+): 4x A100 (40GB) or 2x A100 (80GB), use AWQ/GPTQ

Supported platforms: NVIDIA (primary), AMD ROCm, Intel GPUs, TPUs

Resources

Official docs: https://docs.vllm.ai
GitHub: https://github.com/vllm-project/vllm
Paper: "Efficient Memory Management for Large Language Model Serving with PagedAttention" (SOSP 2023)
Community: https://discuss.vllm.ai

FAQ

What does the serving-llms-vllm skill do?

How do I install serving-llms-vllm?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill serving-llms-vllm in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/serving-llms-vllm/ (personal) or .claude/skills/serving-llms-vllm/ (project).

Which AI agents can use serving-llms-vllm?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is serving-llms-vllm safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains serving-llms-vllm?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 1,067 recorded installs.