Architecture and Signature Optimization
vLLM is built around PagedAttention, an OS-inspired approach that treats the KV cache like virtual memory pages. Instead of allocating a huge contiguous cache per sequence, vLLM slices memory into small blocks that can be shared, recycled, and grown on demand. The result is 14–24× higher throughput than naive Transformer serving for long-context workloads and the best GPU utilization among the three when requests vary widely in length.
SGLang's headline trick is RadixAttention, which automatically reuses KV cache across requests that share a prefix. For workloads where multiple requests start with the same system prompt, few-shot examples, or long shared context — which is most agent workloads, most chat apps, and anything that uses retrieval-augmented generation — RadixAttention turns into measurable latency and cost savings. SGLang also has a compressed finite-state machine for structured output (JSON schema, regex) that runs meaningfully faster than vLLM's guided decoding.
TGI leans on flash attention, continuous batching, tensor parallelism, and a rich quantization menu (GPTQ, AWQ, EETQ, Marlin) to get its numbers. It does not have a single signature algorithm like the other two, but it has the deepest integration with the Hugging Face ecosystem — safetensors, transformers, the Hub, and their managed Inference Endpoints. If your stack already lives inside Hugging Face, TGI removes friction at every layer.
Model Support and Hardware
vLLM supports 100+ model architectures and tends to ship day-one support for new releases from Meta, Mistral, Qwen, and DeepSeek. It runs on NVIDIA (CUDA), AMD (ROCm), and increasingly Intel Gaudi and AWS Neuron. Multi-GPU tensor parallelism is first-class, and speculative decoding plus prefix caching are available. The tradeoff is memory-intensive configuration tuning when context windows grow past 32K.
SGLang matches vLLM on the most popular architectures (LLaMA 3, Mistral, Qwen, Gemma, DeepSeek) and has invested hard in vision-language models — it is frequently the fastest option for serving LLaVA-style multimodal workloads. Hardware support covers NVIDIA and AMD GPUs on Linux. Where vLLM is breadth-first, SGLang is performance-first on a slightly smaller menu.
TGI's model support is the slowest to update of the three — new architectures land weeks later than vLLM — and it is NVIDIA-focused with limited AMD support. But it is the hardest to misconfigure: sensible defaults, Docker-first deployment, and a single-binary operator model mean production teams can ship it without deep CUDA tuning. If "boring and reliable" is a feature, TGI is the one buying it.
Performance in Practice
Public benchmarks swing depending on workload. On long-context single-user streaming, vLLM's PagedAttention wins on throughput. On agent and RAG workloads with heavy prefix sharing, SGLang's RadixAttention pulls ahead by 1.5–3× on real benchmarks. For structured JSON/tool-call output, SGLang's compressed FSM is meaningfully faster than vLLM's and TGI's guided decoding. TGI rarely leads on headline throughput but is competitive once configured and is more consistent under unpredictable traffic.
For most teams, the right mental model is: pick vLLM for general-purpose LLM serving at scale, SGLang when your workload is agents/RAG or needs structured output, and TGI when you want the Hugging Face-endorsed default and operational simplicity. The throughput delta between them is rarely the deciding factor — the ecosystem fit and team skills almost always are.
Verdict
Pick vLLM if you are running LLMs at production scale with varied request patterns, want the deepest model coverage, and have an ML platform team comfortable tuning CUDA knobs. It is the default choice for good reason and will remain competitive as long as PagedAttention stays ahead of the memory efficiency curve.
Pick SGLang if you are serving agents, RAG systems, or anything with shared prefixes — or if structured output latency matters (tool use, JSON extraction, schema-constrained generation). Its RadixAttention and compressed FSM give it a real edge on these workloads, and the 25K+ star community is investing heavily in vision-language support.
Pick TGI if you are already a Hugging Face shop, want one-command Docker deploys on NVIDIA, and value operational simplicity over squeezing the last 10% of throughput. It is the safest choice for enterprise teams who want a supported inference stack and are willing to trade bleeding-edge performance for predictable, boring reliability.