vLLM vs TensorRT-LLM vs SGLang (2026): Which to Pick

Last updated: June 2026 · By Ignacy Kwiecień, founder & editor-in-chief, DecodeTheFuture.org

Which LLM inference engine should you pick in 2026?

Pick by hardware and workload, not by a single throughput number. As of June 2026 the practical default is vLLM because it runs on the broadest hardware and installs with one pip command, exposing an OpenAI-compatible server immediately. Choose SGLang when your traffic re-uses long shared prefixes (RAG with a fixed corpus, multi-turn agents, structured decoding) because its RadixAttention turns that prefix overlap into a cache hit automatically. Choose NVIDIA TensorRT-LLM when you are committed to NVIDIA’s newest silicon (Blackwell, GB200, GB300) and want the deepest kernel-level optimization for it — accepting that you pay for that in build complexity.

This page is strictly about the self-hosted open serving-engine layer — the case where you rent or own the GPU and run the model yourself. It is deliberately not about hosted endpoints (if you just want a URL to call, our best inference APIs comparison for 2026 owns that decision) and not about packaged microservices (NVIDIA NIM bundles an engine, a model and an endpoint into one container — see how NVIDIA NIM works and the NIM alternatives rundown). Read on only if you have already decided to run the engine yourself.

How do PagedAttention, RadixAttention and a compiled engine differ?

The three engines differ at the memory and compilation layer, and that difference is now the main reason to choose one over another. vLLM’s PagedAttention manages the KV cache like an operating system manages virtual memory: it splits the cache into fixed-size pages so GPU memory fragments are near-zero and batches pack tightly. SGLang’s RadixAttention goes one step further by storing KV cache in a radix tree keyed on the token prefix, so two requests that share an opening prefix automatically share its cached computation — no manual cache directives. TensorRT-LLM instead compiles the model into an optimized inference engine tuned to a specific NVIDIA GPU, trading flexibility for kernel-level speed on that hardware.

Here is the practical consequence. PagedAttention is a throughput-and-memory win on almost any workload. RadixAttention adds a second, workload-dependent win that is large when prefixes overlap and negligible when every request is unique. The compiled-engine approach is a hardware-dependent win that is large on the exact GPU it was built for and unavailable everywhere else. The surface feature list has largely converged — per the inference-engineering community framing, all three now implement continuous batching, paged KV caching and FP8 quantization, so the distinction is architectural, not feature-checkbox.

What hardware does each engine run on?

Hardware breadth is the single hardest split between the three, and it is where TensorRT-LLM is the odd one out. vLLM is multi-platform: NVIDIA CUDA, AMD ROCm, Intel XPU and Google TPU (under vLLM V1). SGLang reports native support across NVIDIA, AMD, Intel Xeon, Google TPU and Ascend NPU accelerators, per its official documentation. TensorRT-LLM is NVIDIA-only by design — it compiles CUDA engines and is optimized for Blackwell-class hardware (GB200, GB300). If you run, or might run, on AMD MI300/MI355X, Intel, TPU or Ascend, TensorRT-LLM is off the table before any benchmark; vLLM and SGLang are the only candidates.

Most 2026 comparisons skip this entirely and quietly assume NVIDIA. That assumption is wrong for a growing share of deployments: AMD’s MI300/MI355X line and Ascend NPUs are now first-class targets for both vLLM and SGLang. If you are quantizing aggressively, note that vLLM supports AWQ, GPTQ, FP8 and INT8 weight quantization plus FP8 KV cache, with FP4 on NVIDIA Blackwell and AMD MI300 per AMD’s ROCm guidance — so the low-precision story is no longer NVIDIA-exclusive either.

How should you read the 5x and 25x throughput claims?

Treat the big multipliers as vendor/lab-reported and workload-specific, not as a universal ranking. SGLang’s original “up to 5x higher throughput” figure (from LMSYS) was measured against vLLM v0.2.5 and Guidance v0.1.8 on prefix-sharing workloads (Llama-7B on A10G, Mixtral-8x7B) — exactly the case RadixAttention is built for. It is a real, lab-reported result, but it is not “SGLang is 5x faster than vLLM at everything,” and the vLLM baseline it used is many versions old. On workloads with no shared prefix, the gap shrinks sharply.

The newer “up to 25x” headline is even more conditional. LMSYS reported up to 25x higher performance running DeepSeek R1 on an NVIDIA GB300 NVL72 versus an H200 baseline — but that H200 baseline was latency-constrained at 50 tokens/second per user, and the figure is explicitly an NVIDIA + SGLang collaboration. That makes it an apples-to-oranges hardware-generation comparison (new GB300 silicon vs older H200 under a tight latency cap), not an engine-vs-engine result. Use it to understand that SGLang scales on GB300, not to rank the three engines. The honest takeaway: benchmark on your model, hardware and traffic shape, because every published multiplier was measured on someone else’s.

How hard is each engine to deploy?

vLLM and SGLang install via pip and serve an OpenAI-compatible endpoint in one command; TensorRT-LLM is effectively Docker-image-driven and, per Yotta Labs’ 2026 comparison, costs significantly more developer-hours unless you are already inside the NVIDIA ecosystem. That ergonomics gap is the most under-covered factor in most roundups, and for small teams it often outweighs raw throughput. The reason is the compiled engine: TensorRT-LLM wants you to build an engine artifact tuned to your GPU and model, which is powerful but adds a build-and-version step that pip install vllm simply does not have.

Here is the deploy reality for each. Note that TensorRT-LLM does ship an OpenAI-compatible server (trtllm-serve), so the API surface matches once you are running — the friction is in getting there.

# vLLM: pip install, then serve an OpenAI-compatible endpoint
pip install vllm
vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

# SGLang: same shape, RadixAttention prefix caching on by default
pip install "sglang[all]"
python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-8B-Instruct --port 30000

# TensorRT-LLM: Docker-driven; pull image, then trtllm-serve
docker run --gpus all -it nvcr.io/nvidia/tensorrt-llm/release:1.2.1
trtllm-serve meta-llama/Llama-3.1-8B-Instruct --port 8000

# All three then answer the same OpenAI client call:
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"...","messages":[{"role":"user","content":"hi"}]}'

The takeaway: if your team is not already running NVIDIA’s container stack, vLLM or SGLang will be live in minutes while TensorRT-LLM is still building. If you are on NVIDIA Blackwell and chasing the last increment of per-GPU throughput, that build cost buys you something real.

What is the pick-X-when decision matrix?

Use the matrix below as a decision tree, reading top to bottom — the first row that matches your situation is your answer. It encodes the architecture-first logic above: hardware constraints decide first, then workload shape, then ergonomics.

One nuance the matrix compresses: SGLang and vLLM are not mutually exclusive with the rest of your stack. Both speak the OpenAI API, so you can prototype on vLLM and migrate to SGLang if prefix-cache wins materialize, with no client rewrite. And if KV-cache memory is your bottleneck rather than raw compute, the technique layer matters too — see our explainer on vector quantization for the KV cache for a complementary lever that applies across engines.

How do these engines fit an agent or coding workflow?

For agent and coding workloads, prefix reuse is the decisive factor, which tilts the pick toward SGLang — but only if your framework actually re-sends a stable prefix. Agent loops typically replay a long, fixed system prompt and tool-definition block on every turn; that is precisely the shared-prefix pattern RadixAttention rewards. SGLang also ships Spec V2 as its default speculative-decoding path (with tree drafting across multiple backends as of v0.5.13), which helps the short, latency-sensitive completions common in tool-calling. vLLM remains the safer default when the agent runs across heterogeneous hardware or when you value the larger ecosystem.

The engine is only one layer of an agent stack, though. The orchestration framework on top decides how prefixes are constructed and whether they stay stable — if you are still choosing that, our 2026 guide to AI agent frameworks covers the layer above the engine, and for the developer-tool end specifically, the best AI coding assistants for 2026 rundown covers the products that ultimately call these engines.

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