Which inference engine should I use for personal local AI?

For most users, Ollama is the best starting point. It provides the simplest setup, the widest ecosystem compatibility, and runs well on consumer hardware including Apple Silicon, NVIDIA, and AMD GPUs. If you are on a Mac and want maximum performance, add MLX. If you need native language bindings for application development, consider Mullama.

Which engine is fastest for production GPU serving?

TensorRT-LLM is the fastest on NVIDIA GPUs, delivering 10-20% higher throughput than vLLM. However, vLLM offers 85% of the performance with significantly less operational complexity. For most production deployments under 100 GPUs, vLLM provides the best performance-to-complexity ratio.

Can I switch between inference engines without changing my application?

If your application uses the OpenAI API format, you can switch between Ollama, vLLM, Mullama, and LocalAI by changing the base URL and port. llama.cpp's server mode also supports an OpenAI-compatible endpoint. The main migration concern is model format — GGUF models do not work directly in vLLM or TensorRT-LLM, and vice versa.

What about ExLlamaV2 — is it still relevant in 2026?

Yes. ExLlamaV2 remains the fastest engine for NVIDIA consumer GPUs (RTX 3000/4000/5000 series) with EXL2-format models. If you have a single NVIDIA consumer GPU and want the fastest possible generation speed, ExLlamaV2 is hard to beat. It is less suitable as an API server and lacks the ecosystem integration of Ollama or vLLM.

Local LLM Inference Engines Compared: The Definitive 2026 Guide

The local LLM inference engine landscape in 2026 offers more choices than ever, and selecting the right one can dramatically affect your performance, developer experience, and hardware utilization. This guide compares the seven most important inference engines — Ollama, llama.cpp, vLLM, MLX, TensorRT-LLM, ExLlamaV2, and Mullama — across every dimension that matters. Whether you are a hobbyist running models on a laptop, a developer building AI applications, or an engineer deploying models for a team, this reference covers the tradeoffs.

The Comprehensive Comparison Table

Feature	Ollama	llama.cpp	vLLM	MLX	TensorRT-LLM	ExLlamaV2	Mullama
Primary use	Personal AI server	Low-level inference	Production serving	Mac ML	Enterprise GPU	Fast consumer GPU	Multi-lang integration
Interface	CLI + API	CLI + API + library	Python API + server	Python library	Triton + API	Python library	CLI + API + bindings
Model format	GGUF	GGUF	HF safetensors, GPTQ, AWQ	MLX format	TRT engines	EXL2, GPTQ	GGUF
Setup difficulty	Very easy	Easy-moderate	Moderate	Easy (Mac)	Hard	Moderate	Easy
NVIDIA CUDA	Yes	Yes	Yes	No	Yes (required)	Yes	Yes
AMD ROCm	Yes	Yes	Experimental	No	No	Limited	Yes
Apple Metal	Yes	Yes	No	Yes (required)	No	No	Yes
CPU inference	Yes	Yes	No	No	No	No	Yes
Multi-GPU	Basic	Basic	TP + PP	No	TP + PP	Limited	Basic
Continuous batching	No	No	Yes	No	Yes	No	No
PagedAttention	No	No	Yes	No	Yes	No	No
Speculative decoding	No	Yes (experimental)	Yes	No	Yes	No	No
OpenAI-compatible API	Yes	Yes (server mode)	Yes	Community wrappers	Via Triton	Community wrappers	Yes
Language bindings	Go (+ community)	C/C++ (+ bindings)	Python	Python	Python/C++	Python	Python, Node, Go, Rust, PHP, C/C++
Embedded mode	No	Yes (library)	No	Yes (library)	No	Yes (library)	Yes
Model library	Built-in curated	Manual	Hugging Face	Hugging Face/mlx-community	Manual	Hugging Face	Hugging Face
Fine-tuning	No	No	No	Yes (LoRA/QLoRA)	No	No	No
License	MIT	MIT	Apache 2.0	MIT	Apache 2.0	MIT	MIT

Speed Rankings

Speed depends heavily on hardware and workload. Here are relative performance rankings for common scenarios.

Single-User Generation Speed on NVIDIA RTX 4090 (Llama 3.1 8B, 4-bit)

Engine	Format	tok/s (approx)	Relative
ExLlamaV2	EXL2	~150	Fastest
TensorRT-LLM	TRT engine	~140	93%
vLLM	AWQ	~120	80%
llama.cpp / Ollama	GGUF Q4_K_M	~110	73%
Mullama	GGUF Q4_K_M	~108	72%

Single-User Generation Speed on Apple M4 Max (Llama 3.1 8B, 4-bit)

Engine	Format	tok/s (approx)	Relative
MLX	MLX 4-bit	~82	Fastest
llama.cpp / Ollama	GGUF Q4_K_M	~65	79%
Mullama	GGUF Q4_K_M	~63	77%

Multi-User Throughput on NVIDIA H100 (Llama 3.1 70B, 32 concurrent users)

Engine	tok/s total (approx)	Relative
TensorRT-LLM (FP8)	~6,000	Fastest
vLLM (FP8)	~5,200	87%
Ollama	~150	2.5%

The multi-user scenario highlights the fundamental divide: engines with continuous batching (vLLM, TensorRT-LLM) are an order of magnitude faster for concurrent serving than engines without it.

Engine-by-Engine Analysis

Ollama

Ollama is the most popular entry point into local AI for good reason. A single command installs it, a single command downloads and runs a model. It wraps llama.cpp with a curated model registry, background service management, and an OpenAI-compatible API. The ecosystem integration is unmatched — virtually every local AI tool supports Ollama.

Strengths: Easiest setup, best ecosystem integration, cross-platform, curated model library. Weaknesses: No continuous batching, limited multi-GPU support, wraps llama.cpp (so always slightly slower than raw llama.cpp).

llama.cpp

The foundational project that made local LLM inference practical. llama.cpp provides the inference engine that Ollama, LM Studio, Jan, GPT4All, and many other tools build upon. Using llama.cpp directly (via its llama-server or llama-cli tools) gives you the most control and the latest features before they propagate to wrapper projects.

Strengths: Maximum flexibility, broadest hardware support, most quantization options, foundation of the ecosystem. Weaknesses: More complex setup than Ollama, less polished UX, no continuous batching.

vLLM

The standard for production LLM serving. vLLM’s PagedAttention and continuous batching enable efficient multi-user serving that scales from a single GPU to large clusters. Setup is straightforward for a production tool — pip install vllm and a single command starts the server.

Strengths: Excellent multi-user throughput, PagedAttention, speculative decoding, production-ready. Weaknesses: Requires NVIDIA GPU (ROCm experimental), no CPU inference, more complex than Ollama.

MLX

Apple’s machine learning framework, built specifically for Apple Silicon’s unified memory architecture. MLX provides the best inference performance on M-series Pro, Max, and Ultra chips. It also supports fine-tuning, making it the only engine in this comparison that handles both inference and training.

Strengths: Best Apple Silicon performance, fine-tuning support, Python-native, memory-efficient. Weaknesses: macOS only, Apple Silicon only, smaller model ecosystem, limited tooling integration.

TensorRT-LLM

NVIDIA’s inference optimization platform, delivering the highest throughput on NVIDIA datacenter GPUs. TensorRT-LLM compiles models into optimized execution plans with fused kernels and hardware-specific optimizations. The performance advantage is real but comes with significant setup complexity.

Strengths: Highest NVIDIA GPU throughput, best latency, excellent multi-GPU scaling. Weaknesses: NVIDIA only, complex engine build process, long iteration cycles, steep learning curve.

ExLlamaV2

A high-performance inference engine focused on NVIDIA consumer GPUs with the EXL2 quantization format. ExLlamaV2 achieves the fastest single-user generation speeds on RTX-series GPUs through custom CUDA kernels and efficient memory management. It is the engine of choice for the r/LocalLLaMA community’s power users.

Strengths: Fastest consumer GPU performance, efficient EXL2 quantization, low VRAM usage. Weaknesses: NVIDIA consumer GPUs only, limited API server capabilities, smaller ecosystem, Python-only.

Mullama

A multi-language inference engine that provides native bindings for Python, Node.js, Go, Rust, PHP, and C/C++. Built on llama.cpp, Mullama adds embedded mode (in-process inference without HTTP overhead) and maintains Ollama CLI/API compatibility.

Strengths: Native multi-language bindings, embedded mode, Ollama-compatible, cross-platform. Weaknesses: Pre-1.0 maturity, smaller community, performance similar to llama.cpp.

Decision Framework

Choose Based on Your Scenario

“I just want to chat with local AI” — Use Ollama. Nothing is simpler.

“I am building a Python app with local AI” — Use Ollama for the backend with the OpenAI Python client pointing at localhost. If you need embedded inference, use Mullama or llama.cpp with Python bindings.

“I am building a multi-language application” — Use Mullama for native bindings in your language of choice.

“I need to serve a team of 10+ users” — Use vLLM on NVIDIA GPUs. Continuous batching makes the difference.

“I am running on an M4 Max MacBook Pro” — Use MLX for maximum performance, Ollama for ecosystem compatibility. Run both.

“I have an RTX 4090 and want maximum speed” — Use ExLlamaV2 with EXL2 models for personal use. Use Ollama for API server duties.

“I am deploying at enterprise scale (100+ GPUs)” — Evaluate TensorRT-LLM for throughput-critical workloads, vLLM for everything else.

“I want to fine-tune models on my Mac” — Use MLX. It is the only option here with integrated fine-tuning on Apple Silicon.

Model Format Compatibility Matrix

Format	Ollama	llama.cpp	vLLM	MLX	TensorRT-LLM	ExLlamaV2	Mullama
GGUF	Yes	Yes	No	No	No	No	Yes
Safetensors (FP16)	No	No	Yes	Convert first	Build engine	No	No
GPTQ	No	No	Yes	No	Yes	Yes	No
AWQ	No	No	Yes	No	Yes	No	No
EXL2	No	No	No	No	No	Yes	No
MLX format	No	No	No	Yes	No	No	No
TRT engine	No	No	No	No	Yes	No	No

The model format landscape is fragmented. GGUF is the most portable format for consumer hardware. Safetensors is the interchange format for GPU-focused engines. Specialized formats (EXL2, MLX, TRT engines) offer performance advantages at the cost of portability.

The Bottom Line

There is no single best inference engine — there is only the best engine for your specific combination of hardware, use case, and technical requirements. Ollama remains the best default recommendation for most users. vLLM is the clear choice for multi-user GPU serving. MLX is essential for Apple Silicon power users. TensorRT-LLM and ExLlamaV2 serve niche but important performance-focused roles. Mullama fills the multi-language integration gap. And llama.cpp, the engine behind it all, continues to be the foundation that makes local AI possible on consumer hardware.

The local inference ecosystem in 2026 is mature enough that you can confidently pick the right tool for your needs and trust that it will work well. The hard part is no longer making models run — it is choosing among excellent options.