Best LLM Inference Engines 2026: vLLM, SGLang, TensorRT
The short answer
Start with vLLM; switch to SGLang for agent workloads with long shared prompts; use TensorRT-LLM when you are all-in on NVIDIA and need the last 20–30% of throughput; add NVIDIA Dynamo or llm-d when one model has to scale across many GPUs or nodes. For a laptop or a single developer box, these are the wrong tools — use Ollama or llama.cpp. Versions and features below are from each project’s GitHub on October 19, 2026.
The ranking
| # | Engine | Latest release | Licence | What it is best at |
|---|---|---|---|---|
| 1 | vLLM | v0.32.0 (Oct 18, 2026) | Apache-2.0 | The default: broadest models and hardware, OpenAI- and Anthropic-compatible API |
| 2 | SGLang | v0.5.22 (Oct 19, 2026) | Apache-2.0 | Shared-prefix agents, structured output, multimodal; GPUs, TPUs, NPUs, CPUs, Apple silicon |
| 3 | TensorRT-LLM | v1.2.1 (Apr 20, 2026; main branch active daily) | NVIDIA | Maximum throughput per NVIDIA GPU (NVFP4/FP8, speculative decoding, KV cache compression) |
| 4 | NVIDIA Dynamo | v1.5.1 (Oct 7, 2026) | Open source | Orchestration over vLLM, SGLang or TensorRT-LLM: disaggregated prefill/decode, KV-aware routing |
| 5 | llm-d | v0.10.0 (Sep 29, 2026) | Apache-2.0 | Kubernetes-native distributed serving with tiered prefix cache and wide expert parallelism |
| — | Hugging Face TGI | v3.3.7 (Dec 2025) | Apache-2.0 | Maintenance mode, repo archived — do not start new deployments |
Why each one ranks where it does
1. vLLM. The reference engine. Its feature list covers everything a production server needs: PagedAttention for the KV cache, continuous batching, chunked prefill, prefix caching, speculative decoding (n-gram, suffix, EAGLE, DFlash), disaggregated prefill/decode/encode, and an OpenAI-compatible server that now also speaks the Anthropic Messages API and gRPC. New open models usually ship with vLLM support on day one, which is the strongest practical argument for it.
2. SGLang. Built around RadixAttention, which keeps a tree of cached prefixes so requests that share a system prompt, tool schema or document reuse its KV cache. That is exactly the shape of agent traffic. It runs on NVIDIA B200/B300/GB200 through RTX cards and DGX Spark, AMD, Intel, TPUs and Apple silicon, and plugs into llm-d, Ray Serve and Dynamo for orchestration. Pick it when your benchmark shows high prefix overlap.
3. TensorRT-LLM. NVIDIA’s compiled path: the best numbers on Hopper and Blackwell when you use NVFP4 or FP8, with recent work on KV cache compression and combined guided plus speculative decoding. The cost is NVIDIA lock-in and more build and tuning work per model. Worth it at large, steady volume.
4. NVIDIA Dynamo. Not an engine but the layer above one: it turns vLLM, SGLang or TensorRT-LLM into a multi-node system with prefill and decode in separately scaled GPU pools and routing that sends a request to the replica already holding its KV cache. NVIDIA’s own October 15, 2026 figures for Nemotron 3.5 Super VL on 16 B200 GPUs: KV-aware routing with session affinity and KV offload served up to 8x as many users and cut first-token latency by 85% at the same per-user speed.
5. llm-d. The Kubernetes-native alternative to Dynamo from the vLLM ecosystem: tiered prefix-cache offload to CPU or disk with global KV indexing, wide expert parallelism for big MoE models, and batch APIs. The project reports up to 70% higher tokens per second with prefill/decode disaggregation versus standard vLLM for GPT-OSS on AWS B200 instances.
The optimisations that matter, in plain terms
- Continuous batching — new requests join a running batch every step, so the GPU never waits for the slowest request. Every engine above does it.
- Paged KV cache — memory for attention state is allocated in blocks, so more concurrent requests fit on the same GPU.
- Prefix caching — a shared system prompt or document is computed once and reused. The biggest single win for agents and RAG.
- Speculative decoding — a small draft model proposes tokens the big model verifies in one pass; faster per-user output at low batch sizes.
- Quantization — FP8 and NVFP4 cut memory and raise throughput; check accuracy on your own eval.
- Disaggregated prefill/decode — prompt processing and token generation run on separate GPU pools sized for each; pays off at multi-node scale.
How to choose in 60 seconds
- One model, one to eight GPUs, mixed traffic: vLLM.
- Agents, RAG or chat with long repeated prompts: SGLang (benchmark against vLLM).
- Large steady volume on H100/H200/B200 and an engineer to tune it: TensorRT-LLM, often under Dynamo.
- A big MoE model across many nodes on Kubernetes: llm-d or Dynamo.
- You would rather not run GPUs at all: a managed host — see best LLM inference providers ranked.
- Local development: vLLM vs Ollama vs LM Studio.
The older head-to-head is in vLLM vs SGLang for Llama 5 serving; always rerun throughput tests on your own prompt-length mix, because published benchmarks rarely match production traffic.
Last verified: October 19, 2026. Release versions are from each project’s GitHub releases; performance claims are the projects’ own.