Which inference engine is best for Apple Silicon?

MLX or llama.cpp are the fastest on Apple Silicon. Ollama is the easiest to install.

What is the best local LLM inference server for high throughput?

vLLM, TensorRT-LLM, and SGLang offer the highest throughput on NVIDIA GPUs thanks to PagedAttention, KV-cache optimizations, and continuous batching.

Should I use Ollama or llama.cpp?

Use Ollama for quick local experimentation and a built-in model library. Use llama.cpp when you need lower-level control, speculative decoding, or custom backends.

⚙️

Local LLM Inference Engine Selector

Pick the best backend for your model, hardware, and use case. Ollama, llama.cpp, vLLM, TensorRT-LLM, SGLang, MLX, TGI, TabbyAPI, Aphrodite, and more.

Use case

Hardware platform

Operating system

Preferred quantization

Top priority

Why the backend matters

Throughput vs latency

Chat bots need low time-to-first-token. Batch pipelines care about total throughput. Servers like vLLM and TensorRT-LLM optimize throughput; Ollama and llama.cpp optimize quick local use.

Hardware lock-in

MLX is Apple-only. TensorRT-LLM is NVIDIA-only. llama.cpp is the most portable. Choose the engine that matches the hardware you already own.

Quantization formats

GGUF works nearly everywhere. GPTQ/AWQ/EXL2 are fastest on CUDA. FP16/BF16 need the most VRAM. Match your model file to your engine.

Serving interface

OpenAI-compatible endpoints let you swap local and cloud with one line of code. Pick an engine with an HTTP server if you are building an app.

Check VRAM needs → Compare API vs local cost → Build your agent stack →