llama.cpp vs Ollama vs LM Studio vs Hugging Face vs MLX: What Each One Actually Is

By the RunLocalModel editorial team · Published May 5, 2026 · ~10 minute read

The mental model: a layered stack

Local LLM inference is best understood as a three-layer stack. Almost every confusing question about these tools dissolves once you see which layer each one sits at.

Hugging Face is not even on the running stack - it is the place you got the file from. llama.cpp and MLX are the engines that actually do matrix multiplications on your GPU. Ollama and LM Studio are skins on top of those engines that handle the boring parts (downloading, switching models, serving an API, drawing a chat UI).

Now let us go through them one at a time.

1. Hugging Face: the App Store for AI models

Think of Hugging Face as GitHub for AI models. When Meta releases Llama 3, Mistral releases Mixtral, or Alibaba releases Qwen, they upload the weight files to Hugging Face. Random community members re-upload quantized versions of those weights (the GGUF files you have probably seen). When a guide says "download Llama 3.1 8B Q4_K_M from Hugging Face," it means: go to a page on huggingface.co, find a file ending in .gguf, click download.

Hugging Face also publishes Python libraries (transformers, tokenizers, datasets) that researchers use to load full-precision models in PyTorch. Most desktop users never touch those - they download a single GGUF and hand it to llama.cpp.

We pull our model database from the Hugging Face API at build time, which is why our compatibility checker knows about new models within days of their release.

2. llama.cpp: the engine that started everything

llama.cpp is the project that single-handedly turned local LLMs from "Ph.D. project" into "weekend hobby." Its three big contributions:

• The GGUF format. A single self-contained file that holds the model weights, the tokenizer, and the metadata an inference engine needs. Almost every desktop tool in 2026 uses GGUF.
• K-quants. The clever weight-quantization scheme (Q4_K_M, Q5_K_M, Q6_K, etc.) that lets a 70B model fit on a 48 GB Mac. We covered this in Choosing the Right Quantization.
• Cross-platform GPU acceleration. CUDA on NVIDIA, ROCm on AMD, Metal on Apple, Vulkan and SYCL on others. The same GGUF file runs on all of them through llama.cpp.

You can use llama.cpp directly if you want - it ships a CLI called llama-cli and a server called llama-server. Most people do not, because Ollama and LM Studio do the same thing with much less ceremony.

3. Ollama: llama.cpp with a sensible CLI

Ollama took llama.cpp and added the three pieces of polish that were holding ordinary developers back:

• A model registry at ollama.com/library. Type ollama pull mistral and it downloads a sensible default quantization, no Hugging Face navigation required. (Behind the scenes it is still pulling GGUF files; Ollama just hosts a curated subset.)
• A persistent local server at http://localhost:11434 that exposes a REST API roughly compatible with the OpenAI Chat Completions API. Your own apps can talk to local models with almost zero code change.
• One-line model switching. Pulling a new model and chatting with it is two commands. Comparing two models is trivial.

Ollama uses llama.cpp for inference. When llama.cpp gets faster (new kernels, new quantization), Ollama gets faster automatically. When you set num_ctx in an Ollama Modelfile, it is setting llama.cpp's context size flag. The two are not competitors; Ollama is "llama.cpp for people who want to ship something."

$ ollama pull llama3.1:8b-instruct-q4_K_M
$ ollama run llama3.1:8b-instruct-q4_K_M
>>> Write me a haiku about local LLMs.

4. LM Studio: llama.cpp with a desktop GUI

LM Studio is the friendliest on-ramp to local LLMs in 2026. It bundles a model browser (which queries Hugging Face directly under the hood), a one-click downloader, a built-in chat UI, and a local API server - all behind a polished desktop interface. Like Ollama, LM Studio uses llama.cpp as its primary inference engine. On Apple Silicon, it can also use MLX (more on that next).

The trade-off compared to Ollama is straightforward: more clicks, fewer keystrokes. LM Studio's GUI makes it trivial to A/B compare models or fiddle with sampling parameters visually, but if your workflow is "call a local API from my Python script," Ollama is faster to set up. Many people end up running both - LM Studio for exploration, Ollama for production scripts. We compare them in detail in Ollama vs LM Studio.

5. MLX: Apple's native answer to llama.cpp

MLX is Apple's NumPy-meets-PyTorch for Apple Silicon. It exists because llama.cpp's Metal backend, while excellent, is general-purpose; MLX was written from scratch with Apple's unified memory architecture in mind. The result is meaningfully better throughput on the same hardware - in our testing, an M3 Max running Llama 3.1 8B at Q4 through MLX is typically 10-25% faster than the same model through llama.cpp's Metal backend.

For everyday inference, the practical impact is that LM Studio (and Ollama in some configurations) will increasingly default to MLX on Apple Silicon, and you do not have to think about it. For more ambitious workloads - fine-tuning, custom training, building your own apps on top of LLMs on a Mac - MLX is the framework you would actually program against.

MLX is irrelevant on Windows and Linux. It is Apple-only by design.

Side-by-side cheat sheet

The decision tree

This is the answer for 90% of new users:

1. You want to chat with a local LLM as fast as possible, no coding. → Install LM Studio. Search for a model. Click download. Click chat. Done.
2. You want to call a local LLM from your own Python / Node / curl scripts. → Install Ollama. ollama pull llama3.1, then point your code at http://localhost:11434.
3. You want maximum control, or you want to understand what is actually happening. → Use llama.cpp directly. Build it from source, run llama-server, learn the flags.
4. You are on a Mac and want the absolute best throughput. → Use LM Studio with its MLX backend, or use mlx-lm directly.
5. You want to download a model that no GUI shows you. → Go to Hugging Face, search, download the GGUF, point your runtime at the file path.

Common confusions, sorted out

"Should I use Ollama OR LM Studio OR llama.cpp?"

You are not choosing between them - they overlap. Ollama and LM Studio both use llama.cpp under the hood. Pick the one whose interface fits your workflow. Many people install both and use Ollama for scripting, LM Studio for browsing.

"Does Hugging Face actually run my model?"

No. Hugging Face hosts files. The model runs locally on your hardware via llama.cpp / MLX. Hugging Face also offers paid hosted inference and "Spaces" (hosted demos), but those are different products from the Hub.

"Is MLX an alternative to Ollama?"

No - MLX is at the engine layer (like llama.cpp), not the user-interface layer (like Ollama). The right comparison is "MLX vs llama.cpp," not "MLX vs Ollama." A more accurate framing: LM Studio with MLX backend is an alternative to Ollama for Mac users who want maximum throughput.

"What about vLLM, TGI, ExLlamaV2, KoboldCpp, Jan, GPT4All, ...?"

They all exist. Most are alternative engines (vLLM, TGI, ExLlamaV2) targeting servers and large GPUs, or alternative wrappers (Jan, GPT4All, KoboldCpp) similar in spirit to LM Studio or Ollama. For a desktop user in 2026, llama.cpp + (Ollama or LM Studio) covers the overwhelming majority of use cases.

"Do these tools share downloaded models?"

Sort of. Ollama keeps its own model store; LM Studio keeps its own. If you download Llama 3.1 8B in both, you will use disk space twice. The exception: any tool that takes a path to a GGUF file can read another tool's downloaded file - so you can point LM Studio at a GGUF that Ollama already pulled, and vice versa.

Putting it all together

A typical "I just want to run Mistral 7B on my laptop" workflow in 2026:

1. Open LM Studio (or run ollama pull mistral).
2. The tool fetches a GGUF file - originally uploaded to Hugging Face by the model author or a community quantizer.
3. The GGUF is loaded by llama.cpp (or, on a Mac with the MLX backend selected, by MLX).
4. You chat with the model. Tokens come out at the rate your GPU's memory bandwidth allows.

Five names, one experience. Now you know which name does what.

Related guides on this site

• How to Run Local AI - end to end walkthrough
• Ollama vs LM Studio - deeper comparison
• Choosing the Right Quantization in 2026
• Apple Silicon vs RTX 4090 for Local LLMs
• VRAM vs Unified Memory: When Each Wins
• Best Local AI Models by Use Case (2026 Guide)
• How we estimate VRAM and tokens/second