Best LLM inference engine in 2026

If you are serving open models yourself, the inference engine is the single biggest determinant of your cost per token. These engines are not interchangeable — each optimizes for a different bottleneck.

vLLM — the default

vLLM is the practical default in 2026: broadest hardware support, installs with one pip command, and exposes an OpenAI-compatible server immediately. Its PagedAttention manages the KV cache the way an OS manages virtual memory, splitting it into fixed-size pages so fragmentation is near zero and batches pack tightly. Combined with continuous batching and tensor parallelism, it is the right starting point unless you have a specific reason otherwise.

SGLang — for shared prefixes

SGLang targets structured generation and prompt reuse. Its RadixAttention turns overlapping prefixes into automatic cache hits. If your traffic re-uses long shared prefixes — RAG against a fixed corpus, multi-turn agents, heavy structured decoding — this is a material win that vLLM will not match.

TensorRT-LLM — maximum NVIDIA throughput

TensorRT-LLM goes deepest on NVIDIA kernel optimization, delivering roughly 15–30% higher throughput than vLLM on H100-class hardware. You pay for it in build complexity: models must be compiled per configuration. Worth it if you are committed to current NVIDIA silicon and running at a scale where 20% of GPU spend exceeds the engineering cost.

TGI and llama.cpp

• TGI— Hugging Face's server. Reasonable defaults, tight ecosystem integration, generally behind vLLM on raw throughput.
• llama.cpp — the CPU, Apple Silicon, and edge answer. Not for high-concurrency GPU serving, unbeatable for local and on-device.