Best Local LLMs to Run on Your PC (2026)

First, match a model to your hardware

Before comparing any names, get the memory question right — it decides more than the brand on the box. A quantized model needs roughly as much memory as its file size on disk, plus headroom for the conversation context. The number that matters most is your GPU VRAM: a model that fits entirely in VRAM runs many times faster than one that spills into system RAM. Here is the rough budget:

• No dedicated GPU (CPU + 8–16 GB RAM): stick to small models, roughly 1B–3B, and expect slower replies.
• 6–8 GB VRAM: the popular sweet spot for 7B–8B models at 4-bit — interactive speed, good general quality.
• 12–16 GB VRAM: comfortable with models in the low-teens of billions, or a fast 8B with long context.
• 24 GB VRAM or more: larger models toward the tens of billions, for higher quality and heavier workloads.

For the detailed maths — how quantization changes the figures and how context length eats memory — see our guide on how much VRAM you need for local AI. The golden rule: a capable model that fits will always beat a better one that barely loads.

The families worth considering

You do not need to track every release; a handful of open families cover almost everyone's needs. Each publishes multiple sizes, so the family is a starting point, not a final answer.

• Llama (Meta). The best-known open family and a strong all-rounder, with sizes from tiny laptop models up to large dense and mixture-of-experts builds. Widely supported by every runner. See our dedicated guide to running Llama locally on Windows.
• Qwen (Alibaba). A broad, fast-moving family that consistently ranks well for general use and especially for coding and multilingual work, across a wide spread of sizes.
• Mistral. Known for efficient models that punch above their size, popular where throughput and low latency matter, with capable small and mid-size options.
• Gemma (Google). A lightweight open family with very capable small models, a good pick when you want strong quality on modest hardware.
• Phi (Microsoft). Small models designed to get a lot of capability — particularly reasoning — out of a small footprint, which makes them attractive on limited VRAM. Microsoft's small and large language models reference on Microsoft Learn draws the small-model line at fewer than 10 billion parameters and lists the Phi sizes, noting that smaller models carry smaller storage and memory footprints.
• DeepSeek. Best known for reasoning models that work through problems step by step; the smaller distilled builds run on home hardware. See our guide to running DeepSeek locally on Windows.

Comparison: families vs size and use

The table below is a general map, not a leaderboard. Exact version numbers move fast, so it focuses on the typical size range each family offers and what people most often reach for it to do. Use it to narrow down, then check the current version before you download.

Best for general-purpose chat

For everyday work — drafting emails, summarizing, rewriting, answering questions, brainstorming — a capable general model in the 7B–8B range at 4-bit is the standard recommendation, because it fits an 8 GB GPU and feels interactive. Within that budget, the Llama, Qwen and Mistral families are all sensible all-rounders, and any of them will handle the bulk of daily tasks well. If you have 12–16 GB of VRAM, a model in the low-teens of billions gives noticeably stronger reasoning and handles longer conversations without losing the thread. The honest truth is that at a given size the leading families are close enough that the right answer is usually "whichever fits and you like the feel of" — so try two and keep the one you prefer.

Best for coding help

General models handle everyday coding help — explaining a snippet, writing a small function, spotting a bug — perfectly well, so you do not strictly need a specialised model. Where coding-tuned variants pull ahead is on larger or more specialised tasks and on fill-in-the-middle completion inside an editor. The Qwen and Mistral families are popular choices for code, and several families publish dedicated coding builds worth trying if programming is your main use. A practical tip: pick the largest coding model that fits your VRAM comfortably, since code tasks benefit from the extra capability, and wire it into your editor through the local API — our Llama guide shows how that connection works.

Best small and fast models

Small models have come a long way. Builds in the 1B–8B range now handle drafting, summarizing, rewriting and quick questions genuinely well, and they run fast on modest hardware — some without a dedicated GPU at all. This is where Gemma and Phi shine: both families put real effort into getting strong quality out of a small footprint, and Phi in particular is aimed at good reasoning at small sizes. A small Llama or Qwen is equally at home here. If your priority is a snappy, private assistant that answers instantly and works offline — on a laptop, on battery, on a plane — a good small model is the sweet spot, and it leaves headroom for the rest of your system.

Honest caveats

A roundup owes you the downsides as well as the picks:

• Local models are smaller than the frontier. The best hosted services are still more capable than anything you can run at home. For the hardest tasks, a local model may not be enough — that is a real trade-off, not a detail.
• They make mistakes. Every model here can be confidently wrong. Visible reasoning helps you follow the working but does not guarantee it is correct. Check anything that matters.
• Versions move fast. Families release new generations constantly, and today's best pick may be superseded next month. Treat specific version numbers as perishable and check the current release before downloading.
• Benchmarks are not your workload. Leaderboard scores are a rough guide, not a promise about your tasks. The only test that counts is trying a model on your own prompts.
• Licences vary. "Open weights" is not the same as fully unrestricted open source. The Llama 4 Community License Agreement requires you to display "Built with Llama" and to request a separate licence from Meta once your product passes 700 million monthly active users, and Google's Gemma Terms of Use bind you to a prohibited use policy. If you plan commercial use, read the specific licence for the model you choose.

With those in mind, the good news is that picking is low-risk: models are free, runners are free, and swapping one out for another is a single command or click. Start with something that fits, and adjust from there. For the bigger picture on Windows and on-device AI, see our overview of Windows AI.