vLLM vs SGLang vs TGI: 2026 Inference Engine Benchmark

TL;DR — Which Engine Should You Pick?

If you serve 7B-8B models with shared prefixes (agents, RAG, chat with long system prompts), SGLang wins on throughput by roughly 29% thanks to RadixAttention. If you run large batch jobs on 70B+ models, vLLM is mature, fast, and has the largest ecosystem. TGI is only worth it if you are locked into HuggingFace Inference Endpoints — it trails both on GPU utilization (68-74% vs 85-92%).

The Three Engines at a Glance

Each engine solves the same problem — serve an LLM to many concurrent users efficiently — but with different core innovations. Understanding the one technique each is famous for tells you most of what you need to know about when it wins.

[IMAGE: Side-by-side architecture diagram of PagedAttention vs RadixAttention vs TGI batching]

Benchmark: Throughput on H100

Across published 2026 H100 benchmarks (Spheron, TECHSY, Particula), the throughput picture is consistent. On an 8B model serving ShareGPT traffic, SGLang's RadixAttention delivers roughly 16,200 tokens/sec versus vLLM's 12,500 — a 29% lead that comes from caching repeated prefixes in a radix tree. TGI lands lower still, held back by lower GPU utilization.

The gap flips as model size grows. At 70B parameters the workload becomes compute-bound rather than memory-bound, so PagedAttention and RadixAttention converge, and vLLM's mature batching sometimes edges ahead. If your fleet is mostly 70B+, the engine choice matters less than operational maturity.

For the techniques behind these numbers, see our deep dive on speculative decoding and MLA attention, both of which compose with all three engines.

Benchmark: Latency (TTFT and TPOT)

For interactive workloads, two latency metrics matter. Time-To-First-Token (TTFT) is how long until the user sees the first word; Time-Per-Output-Token (TPOT) governs streaming speed after that. On dual H100 serving Llama 3 70B, both vLLM and SGLang hold TPOT near 20ms with excellent tail control — P99 sits only 30-60ms above P50.

SGLang's TTFT edge comes directly from RadixAttention: when a request shares a prefix with a recent one (common in agents and RAG), the cached states skip prefill entirely. On prefix-heavy traffic this can cut TTFT by up to 6.4x. vLLM added prefix caching in v0.6 and closed much of the gap, but SGLang's radix tree remains more general.

Benchmark: GPU Utilization and VRAM

GPU utilization is the single best predictor of cloud GPU cost efficiency. Under heavy load (100+ concurrent users), vLLM keeps the GPU at 85-92% busy, while TGI manages only 68-74% on the same workload — meaning you pay for roughly 20% more H100 hours to serve the same traffic with TGI. SGLang matches vLLM's utilization while spending fewer cycles on redundant prefix computation.

VRAM footprint is roughly equal across the three for the same model and KV-cache budget — the deciding factor for memory is KV cache quantization, which all three now support. Use our KV Cache Calculator to size your cache before deploying.

When to Pick SGLang

Pick SGLang when your traffic is prefix-heavy: multi-turn agents that resend system prompts and tool schemas, RAG pipelines with shared context, or any workload where many requests overlap in their opening tokens. RadixAttention's prefix tree turns that overlap into free throughput. SGLang also has first-class support for structured output (constrained decoding via regular expressions or JSON schemas), which makes it the default choice for agent tool-calling.

The trade-off: SGLang's ecosystem is younger than vLLM's. Some hardware backends and quantization formats land on vLLM first. If you need bleeding-edge model support day-one, vLLM may serve you sooner.

Learn the core mechanism in our SGLang RadixAttention deep dive (coming next in this cluster).

When to Pick vLLM

Pick vLLM when you want the safest production default. It has the largest community, the most deployment guides, first-class support from every major cloud, and PagedAttention remains a rock-solid memory manager. The v0.6 release delivered a 2.7x throughput improvement and 5x latency reduction over earlier versions, closing most of the gap with SGLang on common workloads.

vLLM is also the best choice when you need maximum compatibility — quantization formats (AWQ, GPTQ, INT4), hardware backends (AMD, Intel, TPU), and observability integrations all land here first. For a step-by-step setup, see our Deploy vLLM in Production guide (coming next in this cluster).

When to Pick TGI

Pick TGI only when you are tightly coupled to the HuggingFace ecosystem — specifically HuggingFace Inference Endpoints, where TGI is the default runtime and switching is friction. For greenfield deployments in 2026, there is little reason to choose TGI over vLLM or SGLang: it trails on throughput, GPU utilization, and prefix caching, and its feature velocity has slowed relative to the other two.

One remaining TGI strength is the lowest operational learning curve if your team already manages HuggingFace model hubs and wants serving to "just work" with HF token auth and model revision pinning. But for any workload where cost-per-token matters, the 20% utilization penalty makes TGI the more expensive choice.

Feature Comparison Matrix

FAQ

Is SGLang faster than vLLM?

On smaller models (7B-8B) and prefix-heavy workloads, SGLang delivers roughly 29% higher throughput than vLLM thanks to RadixAttention. On 70B+ models the gap narrows to 3-5%, and vLLM often wins on raw batch throughput.

Is TGI still worth using in 2026?

Only inside the HuggingFace ecosystem. TGI trails vLLM and SGLang on GPU utilization (68-74% vs 85-92%) and throughput. For new deployments, pick vLLM or SGLang unless HF Inference Endpoints lock you in.

Which engine is best for agents?

SGLang. Agent workloads resend system prompts and tool schemas every turn — RadixAttention caches those shared prefixes, yielding up to 6.4x speedups on multi-turn conversations. Pair it with native structured output for reliable tool calls.

Do all three support speculative decoding?

Yes. vLLM, SGLang, and TGI all ship speculative decoding. See our speculative decoding guide for how draft-verify composes with each engine.

Related Deep Dives

• Speculative Decoding — draft-verify decoding works across all three engines
• MLA Attention — DeepSeek's KV cache compression, compatible with vLLM and SGLang
• KV Cache Quantization — FP8/INT4 cache techniques supported by all three
• KV Cache Calculator — size your cache before deploying any engine