Local LLM Inference Engine Selector
Pick the right backend for your hardware, OS, and use case. Compare Ollama, llama.cpp, vLLM, TensorRT-LLM, SGLang, MLX, and more.
Use-case presets
Not sure how much VRAM you need?
Size your model and quantization first, then match the engine to the format.
VRAM Calculator → Quantization Advisor →Your setup
Last updated: 2026-08-03. See notes.
Engine comparison
| Engine | Ease | Speed | Formats | Platforms | Multi-GPU | Best for |
|---|---|---|---|---|---|---|
| Ollama | Very Easy | Medium | GGUF | macOS, Linux, Windows | — | Fast local experimentation, one-command model downloads, and developers who want a managed CLI/API without tuning. |
| llama.cpp | Medium | Medium | GGUF | macOS, Linux, Windows, iOS, Android, WebAssembly | ✅ | Maximum compatibility across CPUs, GPUs, and edge devices; the engine underneath most GGUF tools. |
| vLLM | Medium | Very High | Safetensors, PyTorch, AWQ, GPTQ, FP8, GGUF (limited) | Linux, Windows (WSL) | ✅ | High-throughput multi-user serving of Hugging Face models with continuous batching and PagedAttention. |
| SGLang | Medium | Very High | Safetensors, PyTorch, AWQ, GPTQ, FP8 | Linux | ✅ | Structured generation, batch programming, and high-performance serving with native regex/JSON constraints. |
| TensorRT-LLM | Hard | Very High | TensorRT-LLM engines (built from HF checkpoints) | Linux, Windows | ✅ | Maximum NVIDIA throughput when you can compile models ahead of time and need production SLAs. |
| MLX (Apple Silicon) | Medium | High | Safetensors, MLX-formatted weights | macOS | — | Apple Silicon Macs (M1–M3 Ultra) running local LLMs efficiently with unified memory. |
| KoboldCpp | Very Easy | Medium | GGUF | Windows, Linux, macOS | — | Storytelling/roleplay and casual chat with a built-in web UI over llama.cpp. |
| text-generation-webui | Medium | Medium | GGUF, Safetensors, GPTQ, AWQ, EXL2, HellaSwag/LoRAs | Windows, Linux, macOS | ✅ | Experimenters who want a web UI supporting many loaders and model types. |
| Aphrodite Engine | Medium | High | Safetensors, GPTQ, AWQ, GGUF | Linux, Windows (WSL) | ✅ | Community-driven serving forked from vLLM, often used for roleplay and creative models. |
| TabbyAPI | Medium | Very High | EXL2, GPTQ | Linux, Windows | — | Self-hosting EXL2/GPTQ models with an OpenAI-compatible API and fast single-GPU serving. |
| llamafile | Very Easy | Medium | GGUF | macOS, Linux, Windows, BSDs, many CPU archs | — | Portable single-file binaries that run on many OSes and architectures with no install. |
| ExLlamaV2 | Medium | Very High | EXL2, GPTQ | Linux, Windows | ✅ | Efficient 4-bit GPTQ/EXL2 inference on NVIDIA GPUs with high single-GPU throughput. |
| Hugging Face Transformers | Medium | Medium | Safetensors, PyTorch, TensorFlow, JAX, GGUF (via helpers) | macOS, Linux, Windows | ✅ | Reference implementation, research, and running the newest models before optimized engines add support. |
| MNN-LLM / MLC-LLM | Hard | Medium | MLC / MNN formats | Android, iOS, Linux, macOS, Windows | — | Mobile and edge deployment (phones, embedded, ARM) where binary size and power matter most. |
Verdict
Select your use case, hardware, OS, and priority to see a recommendation.
Frequently asked questions
Which inference engine is easiest for beginners?▼
Ollama is the fastest path: one command installs it, downloads models, and exposes an OpenAI-compatible API. KoboldCpp and text-generation-webui are also friendly if you want a built-in chat UI.
What is the fastest engine for high-throughput serving?▼
vLLM and SGLang lead for multi-user throughput on NVIDIA GPUs thanks to continuous batching and PagedAttention. TensorRT-LLM can extract even more performance on NVIDIA if you compile engines ahead of time.
What should I use on an Apple Silicon Mac?▼
Use Ollama or llama.cpp for GGUF models, or MLX for native Apple Silicon performance with Safetensors/MLX-formatted weights. MLX usually gives the best tokens-per-watt on M-series chips.
Can I run LLMs on a CPU-only machine?▼
Yes. llama.cpp, Ollama, and llamafile all run on CPU with reasonable latency for small models (1B–3B, or 7B–8B at slower speeds). Use Q4_K_M or smaller quantizations to keep memory and compute low.
Which engine supports the most model formats?▼
Hugging Face Transformers supports the widest range natively. text-generation-webui also loads GGUF, GPTQ, AWQ, and EXL2 through different backends, but is heavier to set up.
VRAM, throughput, and feature estimates are directional. Check the latest release notes of each engine and your specific GPU/CPU/OS combination for exact numbers.