vLLM vs TensorRT-LLM vs Ollama vs SGLang: which should you run?

For most enterprise deployments, vLLM is the right default LLM serving engine: it has the broadest model support, an OpenAI-compatible API, a mature Kubernetes ecosystem and an Apache 2.0 license. Choose TensorRT-LLM (directly or packaged as NVIDIA NIM) when you serve one stable model on NVIDIA GPUs at high volume and can accept a build step for the best raw throughput, and choose SGLang when your traffic shares long prefixes or depends on fast JSON-schema output, as agent and RAG workloads do. Ollama is excellent for developer laptops and prototypes but is not designed for high-concurrency production.

Comparison table: the four engines at a glance

The table summarizes the engines as of 2026; read "typically" as a statement about design, not a benchmark result.

Key takeaway: vLLM is the safe default, TensorRT-LLM buys peak NVIDIA throughput at an operational cost, SGLang wins on shared prefixes and structured output, and Ollama belongs on developer machines.

What each engine does differently

vLLM: PagedAttention and continuous batching

vLLM introduced PagedAttention, which stores the KV cache in fixed-size blocks instead of one contiguous allocation per sequence, so far more concurrent sequences fit in the same GPU memory. Continuous batching admits new requests at every decode step instead of waiting for a batch to finish, which keeps the GPU busy under bursty traffic. The V1 engine, default since 2025, adds automatic prefix caching, chunked prefill and speculative decoding. vLLM runs on NVIDIA, AMD ROCm, Intel, Google TPU and CPU backends and is a PyTorch Foundation project as of 2026; see the vLLM documentation.

A typical launch for a 70B model on four H100 80 GB GPUs with FP8 weights:

vllm serve meta-llama/Llama-3.3-70B-Instruct \
  --tensor-parallel-size 4 \
  --quantization fp8 \
  --max-model-len 32768 \
  --api-key "$VLLM_API_KEY"

Key takeaway: vLLM gives the widest model coverage and the largest operational ecosystem with no compile step.

TensorRT-LLM: compiled engines and Triton

TensorRT-LLM compiles a model into a TensorRT engine that is specific to the GPU architecture, TensorRT version, maximum batch size and maximum sequence length. The compiler fuses kernels, selects tuned attention implementations and applies quantization (FP8, INT8 SmoothQuant, INT4 AWQ and NVFP4 on Blackwell). Its in-flight batching and paged KV cache play the same role as vLLM's continuous batching and PagedAttention.

The price is the build step: a change of GPU model, driver, TensorRT version or sequence-length limit means a rebuild and re-validation. Recent releases add a PyTorch-based runtime that lowers this burden, but the philosophy stays hardware-specific. Serving goes through trtllm-serve (OpenAI-compatible) or Triton Inference Server's TensorRT-LLM backend; see the TensorRT-LLM documentation.

Key takeaway: TensorRT-LLM typically delivers the best raw throughput on NVIDIA hardware; you pay with a build pipeline and NVIDIA-only portability.

SGLang: RadixAttention and fast structured generation

SGLang keeps the KV cache of completed requests in a radix tree keyed by token prefix, so a new request that shares a system prompt, few-shot examples, tool definitions or earlier turns reuses the cached computation automatically. A low-overhead scheduler overlaps CPU scheduling with GPU execution so smaller models are not starved by Python overhead.

For structured output, SGLang compiles JSON schemas, regular expressions and EBNF grammars into a constrained-decoding automaton and uses jump-forward decoding to emit grammar-determined tokens without a forward pass. It supports tensor, data and expert parallelism plus prefill-decode disaggregation across nodes, and it was among the first engines to run DeepSeek V3 and R1 with full optimizations; see the SGLang documentation.

Key takeaway: SGLang typically wins when traffic shares context or must return schema-valid JSON at high rates.

Ollama: developer-friendly single-node serving

Ollama wraps llama.cpp and GGUF models in a one-command experience: ollama pull, ollama run, and an HTTP API with an OpenAI-compatible chat endpoint. It runs on macOS, Linux and Windows with Apple Silicon, NVIDIA and AMD GPUs, and offloads layers to CPU when memory runs out.

It is not a high-concurrency server. Parallel request handling exists (OLLAMA_NUM_PARALLEL), but there is no vLLM-style continuous batching, no multi-node parallelism and no production scheduler, so throughput falls sharply as users are added; see Ollama on GitHub.

Key takeaway: use Ollama on laptops, demos and single-user edge boxes, not behind a load balancer serving hundreds of employees.

NVIDIA NIM: the packaged option

NVIDIA NIM ships each supported model as a prebuilt container with an OpenAI-compatible API, a Helm chart and, on Kubernetes, the NIM Operator. At start-up the container selects an optimized profile for the detected GPU, typically a TensorRT-LLM engine, and falls back to vLLM (or SGLang for some models) where no compiled profile exists, so you get TensorRT-LLM speed without operating the build pipeline.

The trade-off: NIM is licensed under NVIDIA AI Enterprise (free for development through the NVIDIA Developer Program, paid for production; verify current licensing), covers a curated model list, and limits customization to exposed parameters. For regulated enterprises that want signed containers and a support contract, that is often worth it; see the NVIDIA NIM documentation.

Key takeaway: NIM is the fastest route to TensorRT-LLM performance with vendor support, at the cost of licensing and flexibility.

Decision guide by scenario

Sizing interacts with the choice: a 70B model needs about 140 GB of FP16 weights (about 70 GB at FP8) plus 20–50% KV-cache headroom, so plan tensor parallelism across four H100 80 GB, or two H100s or one H200 141 GB at FP8. See the 70B, 405B and DeepSeek R1 sizing table.

Published benchmarks change every release and rarely match your prompt mix, so benchmark on your own traffic before committing: replay real input and output length distributions, fix model, quantization and GPU across engines, use each project's load tool (vllm bench serve, sglang.bench_serving, trtllm-bench) or NVIDIA GenAI-Perf, and sweep concurrency while recording time to first token (TTFT), inter-token latency (ITL) and throughput.

Key takeaway: pick the engine by workload shape and a benchmark on your own traffic, not by a headline number.

Production checklist for any serving engine

The platform around the engine decides uptime. This is the minimum for a production rollout on Kubernetes; the on-premise LLM deployment guide covers the full stack.

1. Health checks: use separate startup, readiness and liveness probes. Loading a 70B checkpoint can take minutes, so give the startup probe a long window and mark the pod ready only after a warm-up request succeeds. vLLM and SGLang expose /health; Triton uses /v2/health/ready.
2. Autoscaling: scale on engine metrics (queued requests, KV-cache utilization, TTFT), not CPU. Use KEDA or a Prometheus-fed metrics adapter and scale up early, because a new GPU pod needs minutes, not seconds.
3. Observability: scrape the engine's Prometheus /metrics endpoint (queue depth, KV-cache usage, TTFT, ITL, throughput), add OpenTelemetry traces at the gateway, and log prompts only with PII controls.
4. Canary releases: send a small traffic share to a new engine version, driver or model revision, compare latency, error rate and a fixed quality evaluation set against the baseline, then promote. Pin container images, model revisions and TensorRT-LLM engine builds.
5. Capacity guardrails: set --max-model-len, maximum concurrent sequences and per-tenant rate limits so overload returns a fast 429 instead of unbounded queueing.
6. Security: keep engines off the public network, terminate TLS and authentication at a gateway, and rotate API keys.

Example Kubernetes probes for a vLLM pod:

startupProbe:
  httpGet: {path: /health, port: 8000}
  periodSeconds: 10
  failureThreshold: 90
readinessProbe:
  httpGet: {path: /health, port: 8000}
  periodSeconds: 5

The startup probe allows 15 minutes of weight loading before Kubernetes restarts the pod. For cluster-level choices, see Kubernetes GPU Operator vs Slurm.

Key takeaway: health probes, metric-driven autoscaling, observability and canaries matter more to uptime than the engine choice itself.

Frequently asked questions

Is TensorRT-LLM faster than vLLM?

On NVIDIA GPUs with a compiled, quantized engine, TensorRT-LLM typically achieves higher throughput and lower latency than vLLM for the same model, because its kernels are fused and tuned for the exact hardware. The gap narrows with every vLLM release and depends on model, quantization and traffic shape, so benchmark both on your own prompts before deciding.

Can Ollama be used in production?

Ollama is production-quality software for its purpose: a single-node runtime for one or a few users. It lacks vLLM-style continuous batching, multi-node parallelism and a production scheduler, so throughput drops quickly as concurrent users increase. It is fine for a handful of users on one workstation; for a company-wide assistant, use vLLM, SGLang or TensorRT-LLM.

What is the difference between vLLM and SGLang?

Both are Apache 2.0 engines with continuous batching, paged KV cache, OpenAI-compatible APIs and multi-GPU support. vLLM has broader model and hardware coverage and the larger ecosystem. SGLang's RadixAttention caches shared prefixes more aggressively, its scheduler has lower overhead and its constrained decoding is faster, which typically favors agentic, multi-turn and JSON-heavy workloads.

Do all four engines expose an OpenAI-compatible API?

Yes, as of 2026: vLLM and SGLang serve /v1/chat/completions and related endpoints natively, TensorRT-LLM offers them through trtllm-serve and Triton's OpenAI frontend, Ollama implements the chat completions subset, and NVIDIA NIM is OpenAI-compatible by design. Coverage of tool calling, vision inputs, logprobs and response_format differs, so test the exact fields your application uses.

Which engine has the best structured output support?

SGLang typically leads, with JSON schema, regex and EBNF grammars enforced by a compiled automaton and jump-forward decoding that skips tokens the grammar already determines. vLLM is close behind with xgrammar, TensorRT-LLM added guided decoding in recent releases, and Ollama supports JSON schema through llama.cpp grammars. All guarantee valid syntax, not correct content, so keep application-level validation.

Does TensorRT-LLM run on AMD or Intel GPUs?

No. TensorRT-LLM and NVIDIA NIM require NVIDIA GPUs and CUDA. For AMD Instinct accelerators, Intel hardware, Google TPUs or CPU-only serving, use vLLM, which maintains backends for these platforms, or SGLang, which supports NVIDIA and AMD. If hardware portability matters, standardize on vLLM and treat TensorRT-LLM as an optional NVIDIA-only optimization.

Does NVIDIA NIM replace vLLM?

No. NIM packages engines rather than replacing them: a NIM container selects an optimized TensorRT-LLM profile for the detected GPU and falls back to vLLM or SGLang where none exists. It adds signed containers, a Helm chart, the NIM Operator and enterprise support under an NVIDIA AI Enterprise license. Teams that want full control, custom models or non-NVIDIA hardware run vLLM directly.

How Nanobase AI can help

Nanobase AI designs, deploys and operates private LLM serving stacks end to end: engine selection and benchmarking on your real traffic, vLLM, TensorRT-LLM, SGLang or NVIDIA NIM deployment on Kubernetes with the GPU Operator, quantization and multi-GPU configuration, and the production layer of health checks, autoscaling, observability and canary releases. We size and install the H100, H200, B200 and RTX PRO infrastructure underneath and connect the serving layer to your systems through MCP servers and RAG pipelines. Headquartered in Silicon Valley and a member of the NVIDIA Inception Program, we work across on-premise, cloud and hybrid environments. Explore our solutions or book a live demo.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.