Lowest Latency LLM Inference Frameworks for Interactive Apps
The short answer
For interactive apps, the framework matters less than the cache. The lowest latency LLM inference frameworks as of October 27, 2026:
- SGLang — best for chat and agent loops that resend long, shared prompts.
- vLLM — best default: the broadest model and hardware support with competitive latency.
- TensorRT-LLM — best raw latency on NVIDIA GPUs, at the cost of a build step and NVIDIA lock-in.
- llama.cpp — best for one user on a laptop, Mac or edge device.
- NVIDIA Dynamo — best when one large model is spread over many GPUs and nodes.
If you would rather not run GPUs at all, our low latency inference providers page compares hosted options (Cerebras, Groq, Fireworks Fast).
The comparison
Versions and GitHub stars read from each project’s repository on October 27, 2026.
| Framework | Latest release | What cuts latency | Hardware | Licence | Pick it when |
|---|---|---|---|---|---|
| SGLang | v0.5.22 (Oct 19, 2026) | RadixAttention prefix cache, fast structured output, speculative decoding | NVIDIA, AMD, others | Apache 2.0 | Multi-turn chat, agents, shared system prompts |
| vLLM | v0.32.0 (Oct 18, 2026) | PagedAttention, continuous batching, automatic prefix caching, speculative decoding | NVIDIA, AMD, TPU, CPU and more | Apache 2.0 | You want one engine for every model |
| TensorRT-LLM | v1.2.1 stable (Apr 20, 2026); v1.4.0rc1 (Oct 22, 2026) | Compiled kernels, in-flight batching, FP8/FP4 | NVIDIA only | Open source (NVIDIA) | Fixed model, NVIDIA fleet, every millisecond counts |
| llama.cpp | v0.7.0 (Oct 15, 2026) | Quantised GGUF models, no server round trip when local | CPU, Apple Metal, Vulkan, CUDA | MIT | Desktop apps, on-device assistants |
| NVIDIA Dynamo | v1.6.0 (Oct 22, 2026) | Disaggregated prefill/decode, KV-aware routing across nodes | NVIDIA (runs vLLM, SGLang or TensorRT-LLM as backends) | Open source (NVIDIA) | Large models over many GPUs |
GitHub stars on October 27, 2026: llama.cpp 132.5k, vLLM 95.7k, SGLang 38.2k, TensorRT-LLM 15.0k, Dynamo 8.4k. LMDeploy (v0.18.0, September 28, 2026) is a credible sixth option, strongest for InternLM and Qwen models.
Where interactive latency comes from
An interactive request has two phases. Prefill reads the whole prompt and produces the first token; decode produces the rest one token at a time. For chat and agents, prefill dominates the wait because prompts are long: system instructions, tool definitions, retrieved documents and conversation history.
That is why prefix caching is the single largest lever. When turn 12 of a conversation resends turns 1–11, a framework that kept their KV cache only has to prefill the new message. SGLang built its design around this (RadixAttention); vLLM’s automatic prefix caching does the same job. Keep the stable part of the prompt — system prompt, tool list — at the start and byte-identical between requests, or the cache misses.
The other levers, in order of impact:
- Batching policy. Continuous batching keeps the GPU busy, but large batches raise every user’s inter-token latency. Cap the batch size for interactive endpoints and route bulk jobs elsewhere.
- Speculative decoding. A small draft model proposes tokens the large one verifies, cutting decode time when acceptance rates are high.
- Quantisation. FP8 or 4-bit weights move less memory per token. Check quality on your own evaluation set.
- Disaggregation. Running prefill and decode on separate GPUs (Dynamo, and the disaggregated modes in vLLM and SGLang) stops long prompts from stalling other users’ streams.
Picks by job
- Customer-facing chat or a copilot: SGLang or vLLM with prefix caching on, a capped batch size and a small model for the first response.
- Coding or tool-calling agents with long, repeated context: SGLang; its prefix reuse pays off on every loop iteration.
- One fine-tuned model on a dedicated NVIDIA fleet: TensorRT-LLM, served through Triton or Dynamo.
- A desktop or offline assistant: llama.cpp with a quantised model; there is no network hop at all.
- A 500B+ parameter open-weight model over several nodes: Dynamo with vLLM or SGLang backends.
- You are choosing between vLLM and SGLang for one model: see vLLM vs SGLang; for local desktop tools see vLLM vs Ollama vs LM Studio.
How to test before you commit
- Replay a sample of real prompts, including long multi-turn ones, at the concurrency you expect at peak.
- Record time to first token and inter-token latency per request and compare the 95th percentile, not the mean.
- Run each framework with its cache and batching settings tuned, not defaults; both vLLM and SGLang ship serving benchmark scripts.
- Re-run after every upgrade: these projects ship releases every few weeks and defaults change.
Last verified: October 27, 2026.