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