vLLM, TGI & LLM Serving Stacks
Comparing industrial high-performance open-source LLM inference serving frameworks.
Deploying open LLMs in production requires specialized high-throughput serving engines. vLLM (UC Berkeley) pioneered PagedAttention and Continuous Batching, establishing the industry standard for high-throughput serving. TGI (HuggingFace Text Generation Inference) offers enterprise security, speculative decoding, and native Safetensors integration. SGLang introduces RadixAttention for automatic KV cache sharing across multi-turn prompts and structured decoding. TensorRT-LLM (NVIDIA) maximizes raw GPU performance using Tensor Core FP8/INT4 kernel optimizations.
The Open LLM Serving Landscape
LLM SERVING STACKS
┌──────────────────────────┬──────────────────────────┬──────────────────────────┬──────────────────────────┐
│ 1. vLLM │ 2. SGLang (LMSYS) │ 3. TGI (HuggingFace) │ 4. TensorRT-LLM │
├──────────────────────────┼──────────────────────────┼──────────────────────────┼──────────────────────────┤
│ Gold standard general │ RadixAttention prefix │ Enterprise production, │ Peak NVIDIA Hopper (H100)│
│ throughput; PagedAttention│ caching; complex structured│ Safetensors, token streaming│ C++/CUDA hardware │
│ & continuous batching. │ prompt workflows. │ security standards. │ performance. │
└──────────────────────────┴──────────────────────────┴──────────────────────────┴──────────────────────────┘
Comparative Feature Matrix
| Feature | vLLM | SGLang | TGI (HuggingFace) | TensorRT-LLM |
|---|---|---|---|---|
| Developed By | UC Berkeley / Community | LMSYS / Stanford | Hugging Face | NVIDIA |
| Key Innovation | PagedAttention | RadixAttention (Prefix Cache) | Production Safetensors | Hardware FP8 C++ Kernels |
| Ease of Setup | Extremely Easy (pip install vllm) | Easy (pip install sglang) | Docker Container | Complex (C++ Compilation) |
| Prefix Caching | Supported (Automatic) | State-of-the-Art (Radix Tree) | Supported | Supported |
| Quantization Support | FP8, AWQ, GPTQ, INT4 | FP8, AWQ, GPTQ | FP8, AWQ, EETQ | FP8, INT4 SmoothQuant |
| Multi-GPU Parallelism | Tensor (TP) & Pipeline (PP) | Tensor (TP) & Pipeline (PP) | Tensor (TP) | Tensor (TP) & Pipeline (PP) |
Production Deployment Checklist
- Model Format: Always serve models in Safetensors format (never raw PyTorch
.binpickles, which execute arbitrary code vulnerabilities). - Metrics & Telemetry: Expose Prometheus metrics (
vllm:num_requests_waiting,vllm:gpu_cache_usage_perc,vllm:time_to_first_token_seconds). - OpenAI-Compatible REST API: Most engines expose standard
/v1/chat/completionsendpoints, allowing drop-in replacement for OpenAI SDK calls.
# Launch vLLM OpenAI-Compatible Server on 2 GPUs
python3 -m vllm.entrypoints.openai.api_server \
--model meta-llama/Meta-Llama-3-70B-Instruct \
--tensor-parallel-size 2 \
--max-model-len 8192 \
--enable-prefix-caching
Say this out loud
"vLLM established the industry benchmark for open LLM serving with PagedAttention and Continuous Batching. SGLang extends this with RadixAttention for automatic KV cache sharing across multi-turn prompts and structured decoding. For maximum performance on NVIDIA H100 hardware, TensorRT-LLM provides deeply optimized C++/CUDA FP8 kernels."
Follow-ups to expect
- What is AWQ (Activation-aware Weight Quantization)? A 4-bit weight quantization technique that protects the 1% most salient weight channels (based on activation magnitudes), preserving model accuracy far better than standard round-to-nearest INT4.
- How does Tensor Parallelism work in vLLM multi-GPU serving? Splits linear projection matrices (QKV, MLP) across $N$ GPUs using Megatron-LM column and row parallel splits, executing All-Reduce operations across NVLink after self-attention and FFN layers.
Check yourself
Question 1 of 3
What core architectural innovation made vLLM the benchmark for high-throughput LLM serving?