# Best Tools for Running LLMs Locally in 2026

> The local LLM stack, ranked by job: Ollama for serving tools, LM Studio and Jan for desktop exploration, llama.cpp for control, vLLM when it's real serving.

Four tools cover local LLMs by job, all on the same GGUF/llama.cpp foundation: Ollama is the developer default (headless server, OpenAI-compatible API every tool targets), LM Studio the polished proprietary desktop app, Jan its open-source equivalent (Apache-2.0, local API on :1337, MCP), and llama.cpp the engine itself for maximum control. Past hobby scale, vLLM is the serving answer.

Running models locally stopped being a hobbyist stunt: privacy-sensitive work, offline use, zero-marginal-cost experimentation, and plain curiosity all justify it, and the tooling matured into a clean stack. The 2026 field is really **one ecosystem** — GGUF models on llama.cpp-family engines — wrapped four ways for four jobs.

## The short list

| Tool | The job | Source |
| --- | --- | --- |
| [Ollama](/tools/ollama) | Local model **server** — back your tools and agents | Open source |
| [LM Studio](/tools/lm-studio) | Polished **desktop** exploration | Proprietary freemium |
| [Jan](/tools/jan) | **Open-source desktop** + local API + MCP | Apache-2.0 |
| [llama.cpp](/tools/llama-cpp) | The **engine** — control, freshness, odd hardware | MIT |

## The picks, by job

**[Ollama](/tools/ollama) — the developer default.** One command pulls and runs a model; a local OpenAI-compatible API makes it the backend every BYO-model tool documents (OpenCode, Cline, Aider, RAG pipelines). Headless, scriptable, boring in the best way. If you install exactly one local tool, it's this.

**[LM Studio](/tools/lm-studio) — the showroom.** The most polished way to *explore*: a catalog with hardware-fit hints, click-to-download, chat, and visible knobs (context, GPU offload, sampling). Proprietary freemium — which is the only reason it shares this tier.

**[Jan](/tools/jan) — the open showroom.** What LM Studio is, but Apache-2.0: model hub, chat, an OpenAI-compatible local API on `:1337`, and MCP support that makes it a tidy fully-local agent host. ~43k stars and 5.7M downloads say the open alternative is no longer the compromise.

**[llama.cpp](/tools/llama-cpp) — the engine room.** Everything above stands on it. Go direct when you want the newest models and features the day they merge, exact backend/quantization control, `llama-server` with minimal footprint, or hardware the wrappers ignore. More flags, more power.

## What's deliberately not on the list

**[vLLM](/tools/vllm)** — because "local" ends where concurrency begins. The moment multiple users, SLOs, or GPU economics enter, you want continuous batching and PagedAttention, not a laptop runtime — [that comparison](/guides/comparisons/vllm-vs-ollama) marks the boundary. And **the model question** is separate from the tool question: whatever you run it in, fit comes down to [quantization](/glossary/quantization) math, and whether to run local at all is the [self-host economics guide](/guides/mlops/self-host-vs-api-llm).

## How to actually choose

Install [Ollama](/tools/ollama) if code is the consumer; add [Jan](/tools/jan) or [LM Studio](/tools/lm-studio) if you want a face on it (open source vs polish is the only real fork — [the head-to-head](/guides/comparisons/ollama-vs-lm-studio) covers it); drop to [llama.cpp](/tools/llama-cpp) when you hit the wrappers' ceilings. The stack is friendly: same models, same format, zero lock-in between layers.

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_Source: https://agentscamp.com/guides/comparisons/best-local-llm-tools-2026 — Guide on AgentsCamp._