Table of Contents
LLM Inference Engines in 2026
Your choice of inference engine determines how many tokens per second your GPU produces, how many concurrent users you can serve, and how efficiently you use VRAM. As of April 2026, the inference engine landscape has consolidated around a few clear leaders, each optimised for different deployment scenarios. Selecting the right engine for your dedicated GPU server can double or triple your effective throughput without changing hardware.
This guide compares the leading engines based on real benchmarks from the tokens per second benchmark tool, focusing on practical throughput, ease of deployment, and model compatibility.
Top Engines Ranked
| Rank | Engine | Developer | License | Best For |
|---|---|---|---|---|
| 1 | vLLM | UC Berkeley | Apache 2.0 | Production serving, high concurrency |
| 2 | SGLang | SGLang Team | Apache 2.0 | Structured output, constrained gen |
| 3 | TensorRT-LLM | NVIDIA | Apache 2.0 | Maximum single-GPU throughput |
| 4 | Ollama | Ollama | MIT | Ease of use, development |
| 5 | llama.cpp | ggerganov | MIT | CPU inference, quantisation |
| 6 | Text Generation Inference | Hugging Face | Apache 2.0 | Hugging Face ecosystem |
Throughput Benchmark Comparison
Tested on an RTX 5090 serving LLaMA 3.1 70B (4-bit quantised) at 10 concurrent users. Updated April 2026:
| Engine | Tokens/sec (total) | First Token Latency | Memory Efficiency |
|---|---|---|---|
| vLLM 0.8.x | 68 tok/s | 125 ms | Excellent (PagedAttention) |
| SGLang | 72 tok/s | 118 ms | Excellent |
| TensorRT-LLM | 78 tok/s | 95 ms | Good (requires compilation) |
| Ollama | 38 tok/s | 210 ms | Good |
| llama.cpp (GPU) | 42 tok/s | 195 ms | Good |
| TGI | 55 tok/s | 145 ms | Good |
TensorRT-LLM achieves the highest raw throughput but requires model compilation and NVIDIA-specific tooling. vLLM offers the best balance of performance and flexibility, while Ollama prioritises simplicity. For detailed multi-engine comparisons, see the vLLM vs Ollama throughput comparison.
Feature Comparison
| Feature | vLLM | SGLang | TensorRT-LLM | Ollama |
|---|---|---|---|---|
| Continuous batching | Yes | Yes | Yes | No |
| OpenAI-compatible API | Yes | Yes | Via Triton | Yes |
| Multi-GPU (tensor parallel) | Yes | Yes | Yes | Limited |
| Quantisation support | AWQ, GPTQ, FP8 | AWQ, GPTQ | All formats | GGUF |
| Setup complexity | Medium | Medium | High | Very Low |
Deployment Complexity
Ollama is the easiest path from zero to inference. One command downloads and serves a model. It is ideal for development, testing, and single-user deployments. For production workloads, vLLM is the standard choice, offering continuous batching, tensor parallelism, and an OpenAI-compatible API out of the box.
TensorRT-LLM delivers the absolute best throughput but requires compiling models into NVIDIA’s engine format, which adds complexity and limits flexibility. SGLang is gaining traction in April 2026 for workloads that need structured output generation with constrained decoding.
Deploy vLLM or Ollama on Dedicated Hardware
Get a GPU server pre-configured for LLM inference. Full root access, optimised drivers, and no per-token charges.
Browse GPU ServersChoosing the Right Engine
For production APIs serving multiple concurrent users, vLLM is the default recommendation. For maximum throughput on NVIDIA hardware where you can invest in compilation, TensorRT-LLM wins. For rapid prototyping or personal use, Ollama gets you running in minutes. For structured output workloads with JSON schemas or constrained generation, SGLang offers unique advantages.
Match your engine choice with the right hardware from the best GPU for LLM inference guide. Compare costs using the LLM cost calculator and review the LLM hosting section for deployment tutorials on open-source LLM hosting.
