home / skills / orchestra-research / ai-research-skills / vllm
This skill helps deploy high-throughput LLM serving with vLLM, enabling OpenAI-compatible endpoints, quantization, and tensor parallelism for production
npx playbooks add skill orchestra-research/ai-research-skills --skill vllmReview the files below or copy the command above to add this skill to your agents.
---
name: serving-llms-vllm
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [vLLM, Inference Serving, PagedAttention, Continuous Batching, High Throughput, Production, OpenAI API, Quantization, Tensor Parallelism]
dependencies: [vllm, torch, transformers]
---
# 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**:
```bash
pip install vllm
```
**Basic offline inference**:
```python
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**:
```bash
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:
```bash
# 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:
```bash
# 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:
```bash
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:
```bash
# 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**
```python
# 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**
```python
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:
```python
# 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**
```python
# 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:
```bash
# Search for AWQ models
# Example: TheBloke/Llama-2-70B-AWQ
```
**Step 3: Launch with quantization flag**
```bash
# 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:
```python
# 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:
```bash
vllm serve MODEL \
--gpu-memory-utilization 0.7 \
--max-model-len 4096
```
Or use quantization:
```bash
vllm serve MODEL --quantization awq
```
**Issue: Slow first token (TTFT > 1 second)**
Enable prefix caching for repeated prompts:
```bash
vllm serve MODEL --enable-prefix-caching
```
For long prompts, enable chunked prefill:
```bash
vllm serve MODEL --enable-chunked-prefill
```
**Issue: Model not found error**
Use `--trust-remote-code` for custom models:
```bash
vllm serve MODEL --trust-remote-code
```
**Issue: Low throughput (<50 req/sec)**
Increase concurrent sequences:
```bash
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:
```bash
vllm serve MODEL --tensor-parallel-size 4 # Not 3
```
Enable speculative decoding for faster generation:
```bash
vllm serve MODEL --speculative-model DRAFT_MODEL
```
## Advanced topics
**Server deployment patterns**: See [references/server-deployment.md](references/server-deployment.md) for Docker, Kubernetes, and load balancing configurations.
**Performance optimization**: See [references/optimization.md](references/optimization.md) for PagedAttention tuning, continuous batching details, and benchmark results.
**Quantization guide**: See [references/quantization.md](references/quantization.md) for AWQ/GPTQ/FP8 setup, model preparation, and accuracy comparisons.
**Troubleshooting**: See [references/troubleshooting.md](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
This skill serves large language models with high throughput and low latency using vLLM's PagedAttention and continuous batching. It provides OpenAI-compatible endpoints, supports quantization (GPTQ/AWQ/FP8), and can run models across tensor-parallel GPUs. Use it to deploy production APIs or run large offline batches while minimizing GPU memory usage.
The skill launches vLLM to host models with a block-based KV cache (PagedAttention) and continuous batching that mixes prefill and decode phases for maximum throughput. It exposes an OpenAI-compatible REST interface, Prometheus metrics, and flags for tensor parallelism and quantization. vLLM handles automatic batching internally and supports prefix caching, speculative decoding, and GPU memory tuning.
How do I reduce out-of-memory errors when loading a model?
Lower --gpu-memory-utilization, reduce max-model-len, or use quantized models (AWQ/GPTQ). Consider tensor parallelism across more GPUs.
What metrics should I watch in production?
Track vllm:time_to_first_token_seconds, vllm:num_requests_running, and vllm:gpu_cache_usage_perc. Aim for TTFT < 500ms and GPU utilization > 80%.