Best Local LLMs to Run in 2026
A hands-on ranking of the top local, open-weight AI models, parameters, country of origin, license, and the hardware each one needs to run on your own machine.
The best local LLMs to run in 2026 are open-weight models you download and run on your own hardware, with no API bill and no data leaving your machine. The strongest small picks are Qwen3, Llama 3.1 8B, Google Gemma 3, Microsoft Phi-4, and OpenAI's gpt-oss-20b. The strongest large open models are DeepSeek, GLM, Kimi K2, and Qwen3's big Mixture-of-Experts builds, but those need a server, not a laptop.
This page ranks the leading families and gives you the numbers that actually decide what you can run: parameter count, country and lab of origin, the exact license, and the rough VRAM each one needs. Local models trade a little quality for privacy, cost control, and offline use, the tradeoff most buyers care about.
There is no single 'best' local model. The right pick depends on your hardware and your task. Use the ranking table to shortlist by size and license, then the guidance below to match one to your GPU and your use case.
The best local LLMs, ranked by what they run best on
The best local LLM for most people is a small open-weight model that fits their GPU, a 4B to 14B model runs on a typical gaming card. The table below groups the leading open-weight families by size, license, and best use, so you can shortlist before you download anything.
Parameter counts for Mixture-of-Experts (MoE) models are shown as total / active. The total is the download size; the active figure is what runs per token. A model's total size still sets its memory footprint, so a 671B MoE is a server model even though only 37B are active at once.
| Model family (latest open build) | Parameters | Lab · Country | License type | Smallest local build (~VRAM at Q4) | Best for |
|---|---|---|---|---|---|
| Qwen3 | 0.6B–32B dense; 30B/3B & 235B/22B MoE | Alibaba · China | Apache 2.0 (true open source) | 4B ≈ 3–4 GB; 8B ≈ 5–6 GB | Best all-round: reasoning, coding, multilingual |
| Llama | 8B–405B dense; Llama 4 MoE 17B active | Meta · USA | Llama Community License (open-weights, restricted) | 3.2 1B/3B tiny; 3.1 8B ≈ 5–6 GB | General chat, the biggest tooling ecosystem |
| Gemma 3 | 270M–27B dense, multimodal | Google · USA | Gemma Terms of Use (open-weights, restricted) | 1B < 1 GB; 4B ≈ 3–4 GB | On-device, vision, 140+ languages |
| Phi-4 | 3.8B & 14B dense | Microsoft · USA | MIT (true open source) | mini 3.8B ≈ 3 GB; 14B ≈ 9–10 GB | Small-model reasoning and math |
| gpt-oss | 21B/3.6B & 117B/5.1B MoE | OpenAI · USA | Apache 2.0 (true open source) | gpt-oss-20b runs in ≈ 16 GB | OpenAI-family open reasoning, local-friendly |
| Mistral | 7B/24B dense; Mixtral MoE | Mistral AI · France | Apache 2.0 core (Codestral is non-commercial) | Mistral 7B ≈ 5 GB; Small 24B ≈ 14–16 GB | Permissive licensing, efficiency, EU data |
| DeepSeek | 671B/37B MoE (distills 1.5B–70B) | DeepSeek · China | MIT weights (commercial OK) | R1-Distill 8B ≈ 5–6 GB; full model server-only | Frontier reasoning; run the distills locally |
| GLM | 355B/32B & 106B/12B MoE; Z1 dense 9B/32B | Zhipu AI (Z.ai) · China | MIT (true open source) | Z1 9B ≈ 6 GB; 4.5-Air needs a big rig | Agentic work and coding |
| Kimi K2 | 1T/32B MoE | Moonshot AI · China | Modified MIT (commercial OK) | Server-class only | Agentic tool-use and coding at scale |
| IBM Granite 4.0 | 350M–32B; hybrid Mamba/MoE | IBM · USA | Apache 2.0 (true open source) | Nano < 1 GB; 3B ≈ 2 GB | Enterprise, tool use, tiny on-device builds |
Rankings shift monthly as new versions ship. For a live head-to-head, check community leaderboards like the LMArena human-preference board and the Hugging Face Open LLM Leaderboard rather than any single frozen list.
Choosing a local model for a team or a regulated workflow? We help you match open-weight models to your hardware, data rules, and use case, then get them running.
Book a ConsultationHow the leading open models score on real-world coding
The clearest way to rank open models on a hard task is SWE-bench Verified, which measures how often a model fixes a real GitHub issue. The chart below shows the score each lab reported for its flagship open model at release.
Read it with two cautions. First, labs run these tests under different scaffolding, so treat the numbers as a guide, not a photo finish. Second, the top scorers are mostly server-class: Kimi K2, GLM, and MiniMax need multiple data-center GPUs. The bars marked as local, gpt-oss-20b especially, are the ones you can actually run at home.
For a page focused on what runs on consumer hardware, the practical takeaway is that a 20B–32B open model now clears 60% on this benchmark. That was frontier-only territory a year earlier.
SWE-bench Verified (agentic coding), accuracy each lab reported for its flagship open-weight model at release. Conditions vary between labs; verify current standings on live leaderboards.
How much VRAM you need to run a local LLM
You need roughly 0.5 to 0.6 GB of VRAM per billion parameters at 4-bit (Q4) quantization, plus 20–30% for context and overhead. That simple rule tells you what fits before you download anything.
Quantization is the key trick: it shrinks a model's numbers from 16-bit to 4-bit, cutting memory roughly four times with only a small quality loss. Almost everyone runs local models quantized. The table below maps common sizes to the card you need.
| Model size (Q4) | Rough VRAM to run | Typical hardware |
|---|---|---|
| 7–8B | ~5–8 GB | Any 8 GB gaming GPU |
| 13–14B | ~8–16 GB | 12–16 GB GPU |
| 30–34B | ~20–24 GB | One 24 GB card (RTX 3090/4090) |
| 70B | ~35–48 GB | Two 24 GB cards or one 48 GB card |
No dedicated GPU? You can still run 7B–13B models on your CPU and system RAM through llama.cpp, you need about the same number of gigabytes free in RAM, but it runs much slower. On an Apple Silicon Mac the memory is unified, so all of it counts as usable VRAM: 16 GB handles entry-level local models, 32 GB runs a 30B comfortably, and 64 GB makes a 70B feasible.
To size your exact setup, use our local AI hardware calculator, enter your GPU and it tells you which models will fit.
Country of origin: why the flag on a model matters
Country of origin matters for local models because it drives license terms and vendor-risk questions, not because the weights behave differently once downloaded. The strongest open models now come from three blocs: the United States, China, and France.
US labs (Meta, Google, Microsoft, OpenAI, NVIDIA, IBM) ship a mix of truly open licenses and restricted community terms. China's labs (Alibaba, DeepSeek, Moonshot, Zhipu) have released many of the most capable weights under genuinely permissive licenses like Apache 2.0 and MIT. France's Mistral is the European standout for clean, permissive licensing.
When we evaluate open-weight models for client deployments, the license fine print, not the benchmark, is what most often rules a model out, and for regulated firms the model's jurisdiction of origin drives a real data-governance review. If that decision applies to you, our guide to American open-weight AI models covers the US-built options in depth.
- USA: Meta Llama, Google Gemma, Microsoft Phi, OpenAI gpt-oss, NVIDIA Nemotron, IBM Granite.
- China: Alibaba Qwen, DeepSeek, Moonshot Kimi, Zhipu GLM, 01.AI Yi.
- France: Mistral and its Mixtral and Codestral lines.
- Canada and UAE: Cohere Command (non-commercial license) and TII Falcon.
How to run a local LLM today
The fastest way to run a local LLM is Ollama: install it, then type one command like 'ollama run qwen3' and it downloads and runs the model. It exposes an OpenAI-style API too, so your own apps can call it.
If you would rather click than type, LM Studio gives you a full desktop app with a built-in model browser. Both are free and run on Mac, Windows, and Linux. For the full walkthrough, see our guides to Ollama and LM Studio, or the runner comparison in Ollama vs LM Studio.
Want a specific model? We have step-by-step local setup guides for Llama, Qwen, DeepSeek, Gemma, Mistral, and Phi.
Local model or cloud model: which should you use?
Use a local model when privacy, offline access, or predictable cost matter more than raw peak quality. Use a cloud model like ChatGPT or Claude when you need the strongest possible answer and do not want to manage hardware.
| Factor | Local open-weight model | Cloud model (ChatGPT / Claude) |
|---|---|---|
| Data privacy | Stays on your machine | Sent to a vendor API |
| Cost | One-time hardware | Per-token, ongoing |
| Peak quality | Very good, a step behind | Frontier |
| Works offline | Yes | No |
| Setup effort | You install and maintain it | None |
For the full breakdown of when each wins, see local LLM vs ChatGPT.
Frequently Asked Questions
- For most people the best local LLM is Qwen3 in a size that fits their GPU, the 4B and 8B builds run on ordinary hardware and are strong all-round. If you have 16 GB of memory, gpt-oss-20b is an excellent local reasoning model. Rankings shift often, so check a live leaderboard before you commit.
- Yes. You can run 7B to 13B models on your CPU and system RAM using llama.cpp or Ollama. You need roughly the same number of gigabytes free in RAM as you would in VRAM, but responses are much slower than on a GPU. On an Apple Silicon Mac the unified memory makes this far more practical.
- With 16 GB of VRAM you can comfortably run models up to about 14B at Q4, plus room for context. Strong picks are Qwen3 14B, gpt-oss-20b (which is memory-efficient), Gemma 3 12B, and Phi-4. Use our hardware calculator to confirm a specific model fits before downloading.
- The models themselves are free to download and run. You pay only for electricity and the hardware you already own. Check the license, though: most (Qwen, Mistral, Phi, gpt-oss) are truly open under Apache 2.0 or MIT, while some (Llama, Gemma) allow commercial use with restrictions, and a few (Cohere Command) are non-commercial only.
- The best open-weight models are close to frontier cloud models on everyday tasks but still trail on the hardest reasoning and the very largest context. The gap keeps narrowing. For privacy, offline use, and cost control a local model often wins overall, even if its single-answer quality is a step behind ChatGPT or Claude.
- For coding on local hardware, the strongest picks are Qwen3's coder builds, DeepSeek's distilled coders, and Mistral's Devstral Small. See our dedicated guide to the best local LLM for coding for a ranked breakdown by VRAM and coding harness.
Not sure which local model fits your hardware and use case?
Layer3 Labs helps teams pick, deploy, and secure open-weight models on their own infrastructure, privately, and without the per-token bill. We map your hardware, data, and workload to the right model.
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