Ollama vs LM Studio vs vLLM vs llama.cpp: quale scegliere nel 2026?

“What should I use to run LLMs locally?” is the most common question in local AI, and the honest answer is: it depends on whether you’re one developer prototyping or a team serving thousands of requests. These four tools are not really competitors — they solve different problems. This guide sorts out which is which.

They’re not the same kind of thing

The single biggest source of confusion is treating these as four versions of one product. They sit at different layers of the stack:

• llama.cpp and MLX are engines — the low-level code that runs the math of a quantized model on your hardware.
• Ollama and LM Studio are experience layers — they both wrap llama.cpp (and increasingly MLX on Mac) and add model management, a friendly interface, and an API.
• vLLM is a serving system — built from the ground up for high-throughput GPU serving, not local-first development.

Once you see it this way, the choice gets simpler: pick the layer that matches your job.

Head-to-head comparison

The performance gap that matters

For a single user typing one prompt at a time, all four feel fast. The differences explode the moment you send concurrent requests.

In 2026 production benchmarks, vLLM’s architecture — PagedAttention plus continuous batching — pulls dramatically ahead under load. At peak throughput, community tests put vLLM at roughly 793 tokens/sec versus Ollama’s ~41 tokens/sec, with P99 latency at peak of about 80 ms for vLLM against 673 ms for Ollama. That’s the 16–20× gap people quote, and it’s real — but it only appears when many users hit the model at once.

The lesson: throughput numbers measure a serving problem, not a prototyping problem. If you’re the only user, Ollama’s “slower” number is irrelevant — you’ll never notice it.

Apple Silicon changed the math in 2026

If you’re on a Mac, there’s a recent twist. On March 30, 2026, Ollama announced its Apple Silicon path is now powered by MLX rather than just the Metal llama.cpp backend. The speedup was large: on an M5 Max running Qwen 3.5, prefill jumped about 57% and decode roughly 93% faster than the previous build. LM Studio also offers an MLX path. For Mac users, this narrowed the single-user speed gap considerably and made Ollama and LM Studio genuinely fast, not just convenient.

Which one should you actually pick?

Pick Ollama if you’re a developer who wants to prototype, script against an API, and not think about infrastructure. It’s the lowest-regret default and the easiest to automate. Start here — read our guida completa a Ollama if you’re new to it.

Pick LM Studio if you want a graphical app to discover, download, and chat with models without touching a terminal — especially on a Mac or Windows laptop. It’s the best “just let me click around” experience.

Pick vLLM if you’re putting a model in front of real users and need to serve many requests per second. The setup cost is real, but nothing else matches its concurrent throughput.

Pick llama.cpp directly if you need the absolute fastest single-stream inference, are deploying to embedded or unusual hardware, or want to embed inference in your own binary.

A common and sensible path: prototype on Ollama, ship on vLLM. You validate the idea with zero friction, then move the proven workload to a serving stack when concurrency demands it. To choose the right model to run on either, see our pick of the best local LLMs in 2026.

Hardware and OS compatibility: which one even runs on your machine

Performance only matters if the tool runs on your hardware in the first place. This is where the four diverge most sharply, and it is the question that should narrow your shortlist before you ever look at benchmarks. The deciding factors are your GPU vendor, whether you are on Windows, and how much you are willing to fight a driver stack.

If you are on Windows with an NVIDIA card, all four can work, but only three are pleasant. Ollama, LM Studio, and llama.cpp install in minutes with native CUDA support. vLLM has no official Windows build and never has — you run it through WSL2, Docker, or an unofficial community fork. For most Windows users, that alone rules vLLM out for casual use.

If you have an AMD GPU, the picture is more forgiving than it used to be, largely thanks to Vulkan. LM Studio leans on a Vulkan backend that delivers acceleration on AMD and even Intel integrated graphics across Windows and Linux, which makes it the easiest AMD path. llama.cpp is the most flexible of all: it ships CPU, CUDA, ROCm/HIP, Metal, Vulkan, and Intel SYCL backends, so almost any GPU can be made to work if you are comfortable compiling. Ollama supports AMD via ROCm — solid on Linux, more limited on Windows, where ROCm covers only discrete Radeon RX/PRO cards — with experimental Vulkan filling the gaps. vLLM’s AMD story is centered on datacenter Instinct accelerators (MI300X and newer), which are now a first-class target; consumer Radeon support exists but remains secondary and rougher to set up.

If you are CPU-only or on integrated graphics, llama.cpp and the tools built on it (Ollama, LM Studio) all run, just slowly. vLLM has an experimental CPU path but was never designed for single-user interactive use on this kind of hardware.

The takeaway: if your hardware is anything other than a recent NVIDIA card on Linux, LM Studio or llama.cpp will almost always get you running with the least friction, and vLLM should be reserved for the NVIDIA (or Instinct) servers it was built for.

Domande frequenti

Is vLLM faster than Ollama?

Under concurrent load, dramatically — roughly 16–20× higher throughput in 2026 benchmarks, because vLLM was built for serving with PagedAttention and continuous batching. For a single user sending one request at a time, the difference is negligible. vLLM’s advantage is throughput, not single-prompt latency.

Is LM Studio better than Ollama?

For non-developers, often yes — LM Studio’s GUI makes browsing and running models effortless with no terminal. For developers who want to script, automate, or integrate a local model into an app, Ollama’s CLI and API are more flexible. They’re built on the same engine, so model quality is identical.

Do Ollama and LM Studio use llama.cpp?

Yes. Both are experience layers that wrap llama.cpp (and Apple’s MLX on Apple Silicon). That’s why they run the same GGUF models at similar speeds — the underlying engine is shared. The difference is the interface and the management features around it.

What about llama.cpp vs Ollama directly?

llama.cpp is the engine; Ollama is a friendly wrapper around it. Running llama.cpp directly gives you the fastest single-stream performance and the most control, at the cost of doing the setup, model conversion, and flag-tuning yourself. Ollama trades a little speed for enormous convenience.

Which is best for production?

vLLM, clearly, if “production” means serving multiple concurrent users on GPUs. Ollama is fine for low-traffic internal tools or single-user desktop apps. llama.cpp can be productionized with effort. LM Studio is a desktop tool and not meant for server deployment.

Can I run these tools on an AMD GPU?

Yes, with caveats. LM Studio is the easiest path on consumer AMD cards thanks to its Vulkan backend, which also accelerates Intel integrated graphics. llama.cpp supports AMD through both ROCm and Vulkan if you are willing to compile. Ollama uses ROCm — reliable on Linux, more limited on Windows, where it covers only discrete Radeon RX/PRO cards — with experimental Vulkan as a fallback. vLLM’s AMD support is built around datacenter Instinct accelerators; it can run on consumer Radeon cards, but that path is secondary and harder to configure.

Can I run vLLM on Windows?

Not natively. vLLM has never shipped an official Windows build and there is no public roadmap for one. The supported routes are WSL2 with NVIDIA GPU passthrough, Docker (including Docker Model Runner’s WSL2 backend), or an unofficial community fork. If you want a native Windows experience, choose Ollama, LM Studio, or llama.cpp instead.

What is the difference between GGUF and safetensors models?

GGUF is the quantized, single-file format used by llama.cpp, Ollama, and LM Studio — it bundles weights, tokenizer, and config together for fast loading on laptops and edge devices. Safetensors is the Hugging Face format that vLLM expects by default, typically holding full or lightly-quantized weights for server GPUs. vLLM can load GGUF, but its own docs call that path highly experimental and under-optimized; for the llama.cpp-based tools, GGUF is the native format.

Conclusione

Stop thinking of these as four competing products and start thinking of them as four jobs. Ollama is the on-ramp, LM Studio is the GUI, vLLM is the server, and llama.cpp is the engine underneath. For most people reading this, the answer is: start with Ollama today, and reach for vLLM the day concurrency — not curiosity — becomes your constraint.