Inference Engine Comparison

Compare vLLM, SGLang, TensorRT-LLM and llama.cpp features and use cases

Quick Selector Quiz

Answer 3 questions to get a recommendation

1. What hardware do you have?

2. What matters most to you?

3. What is your deployment scale?

Core Metrics

Engine	Throughput	Latency (5=low)	Ease of setup (5=easy)	Hardware support	Quantization	Best for
vLLM	●●●●●	●●●●●	●●●●●	NVIDIA GPU, AMD GPU, CPU, TPU	FP8, INT8, INT4 (AWQ), GPTQ, GGUF (limited)	High-concurrency production serving with an OpenAI-compatible API
SGLang	●●●●●	●●●●●	●●●●●	NVIDIA GPU, AMD GPU	FP8, INT8, INT4 (AWQ), GPTQ	Complex prompt pipelines, structured output and multi-turn prefix reuse
TensorRT-LLM	●●●●●	●●●●●	●●●●●	NVIDIA GPU	FP8, INT8, INT4 (AWQ), NVFP4	NVIDIA-only shops chasing maximum performance with engineering capacity
llama.cpp	●●●●●	●●●●●	●●●●●	CPU, NVIDIA GPU, AMD GPU, Apple Silicon	GGUF Q2, GGUF Q4, GGUF Q5, GGUF Q6, GGUF Q8	Local / edge devices, personal computers, CPU or Mac inference

Feature Matrix

✓ Supported · ~ Partial · ✗ Not supported

Feature	vLLM	SGLang	TensorRT-LLM	llama.cpp
Continuous batching	✓	✓	✓	~
OpenAI-compatible API	✓	✓	~	✓
Prefix caching	✓	✓	✓	~
Structured / constrained output (JSON)	✓	✓	~	✓
Tensor parallelism (multi-GPU)	✓	✓	✓	~
Speculative decoding	✓	✓	✓	✓
Multimodal (vision) models	✓	✓	✓	~
LoRA adapter hot-loading	✓	✓	✓	~
CPU-only inference	~	✗	✗	✓
Apple Silicon (Metal)	✗	✗	✗	✓
AMD GPU (ROCm)	✓	✓	✗	✓
Single pip install	✓	✓	✗	✓

Decision Flowchart

Start: where will you run the model?
│
├─ CPU only / Mac / edge device?
│   └─ Yes → llama.cpp (GGUF quantization, single binary)
│
└─ GPU server available?
    │
    ├─ Non-NVIDIA (AMD ROCm)?
    │   └─ Yes → vLLM or SGLang (TensorRT-LLM is NVIDIA-only)
    │
    └─ NVIDIA GPU?
        │
        ├─ Need maximum performance and can invest engineering effort?
        │   └─ Yes → TensorRT-LLM (with Triton Inference Server)
        │
        └─ No → what is the main workload?
            │
            ├─ Multi-turn chat / structured output / heavy prefix reuse
            │   └─ → SGLang (RadixAttention advantage)
            │
            └─ General high-concurrency API serving
                └─ → vLLM (most mature ecosystem, safe default)