BEST LOCAL AI MODELS
The best AI models to run on your own machine in October 2026 are Gemma 4 12B on a 16 GB laptop, Qwen3.8 27B on a 24 GB GPU or 32 GB+ Mac, and Qwen3.8-Flash-Next on a gaming PC with 64 GB of RAM. Below: 11 models ranked by hardware tier, with install commands, memory needs and the source behind every claim.
Last updated: October 8, 2026. Re-ranked for Qwen3.8 and Gemma 4; every install command, download size, benchmark and license re-checked against the linked model cards and Ollama pages.
Quick answer
8 GB laptop? Gemma 4 E4B. 16 GB laptop? Gemma 4 12B. 24 GB GPU or 32 GB+ Mac? Qwen3.8 27B. Gaming PC with a 12 GB card and 64 GB of RAM? Qwen3.8-Flash-Next through Strata. Install the Ollama picks in one command with Ollama. Coding only? See the best local coding LLMs.
BY HARDWARE TIER
Laptop (8-16 GB RAM)
Models that run on standard laptops, including MacBooks with 8-16 GB of unified memory and Windows/Linux laptops with entry-level GPUs.
Desktop (24-64 GB, dedicated GPU)
Models that need a 12-24 GB GPU like an RTX 3090/4090/5070, or an Apple Silicon Mac with 32 GB+ of unified memory.
Workstation (96 GB+)
Models that need multiple GPUs or an Apple Silicon machine with 96 GB+ of unified memory.
Qwen3.8 27B
AlibabaQwen's August 2026 dense model posts 61.7 on SWE-bench Pro in Qwen's own model card, against 53.4 for Opus 4.6 Max, with a native 262,144-token context and thinking on by default. The default Ollama build is an 18 GB download with a 256K context window. The numbers are vendor-run, Opus still leads on HLE, Terminal Bench and NL2Repo in the same table, and a community tester found its KV cache memory-hungry at long context.
Verdict: Our first pick if you have a 24 GB GPU or a 32 GB+ Mac and want a local coding agent.
ollama run qwen3.8:27bSource: Qwen3.8-27B model card - Qwen3.8 27B for local agentic coding
Gemma 4 12B
GoogleGoogle released the 12B on June 3, 2026 for laptops: it runs in 16 GB of VRAM or unified memory, takes text, image and audio input, and Google says it scores near the 26B MoE on standard benchmarks at less than half the memory footprint. The model card lists 72.0% on LiveCodeBench v6 and a 256K context. The Ollama build is a 7.7-8.0 GB download.
Verdict: Our pick for a 16 GB laptop.
ollama run gemma4:12bSource: Google: Introducing Gemma 4 12B - Gemma 4 developer guide
Gemma 4 26B A4B
GoogleOnly 3.8B of its 25.2B parameters run per token, so it answers at small-model speed. Google's model card lists 77.1% on LiveCodeBench v6 and a 256K context, and Ollama's build takes text plus image input. On an 8 GB Mac, the open-source TurboFieldfare engine runs this same model in about 2 GB of RAM by streaming experts from the SSD; its README measures 5.1-6.3 tokens per second on an 8 GB M2 MacBook Air.
Verdict: The pick when response speed matters more than peak coding scores.
ollama run gemma4:26bSource: Gemma 4 model card - Gemma 4 26B in 2 GB of RAM on a Mac
Qwen3.8-Flash-Next (via Strata)
AlibabaQwen's 125B mixture-of-experts model activates 6B parameters per token and posts 62.5 on SWE-bench Pro in Qwen's own table. The MIT-licensed Strata installer fits it on a Windows or Linux gaming PC by keeping the busiest experts on the GPU and the rest in system RAM; its README measures 53-94 tokens per second on an RTX 5070 (12 GB) with 64 GB of RAM, depending on the quant. On Windows, run START-HERE.bat instead of setup.sh. Those fast builds are 2-3 bit, not the full model the benchmarks describe, and the license requires a separate agreement with Qwen for some commercial uses.
Verdict: Worth it if you already own a 12 GB card and 64 GB of RAM. Read the license before you ship on it.
git clone https://github.com/Niko1221/Strata && cd Strata && ./setup.shSource: Strata README - Run Qwen3.8-Flash-Next on a gaming PC
Gemma 4 31B
GoogleThe dense flagship of the family scores 80.0% on LiveCodeBench v6 in Google's model card, the highest of the Gemma 4 sizes, with a 256K context and image input. All 30.7B parameters run on every token, so it is slower than the 26B MoE on the same hardware.
Verdict: Choose it over the 26B when quality matters more than speed.
ollama run gemma4:31bSource: Gemma 4 model card
Gemma 4 E4B
GoogleThe edge model of the Gemma 4 family takes text, image and audio, has a 128K context, and scores 52.0% on LiveCodeBench v6 in Google's model card. It is what a bare 'ollama run gemma4' pulls; Ollama lists it at 6.6-9.5 GB depending on the build, and the Q4_K_M build is 6.6 GB.
Verdict: The pick for 8 GB machines.
ollama run gemma4:e4bSource: Gemma 4 model card
DeepSeek R1 Distill Qwen 32B
DeepSeekA Qwen-based 32B model distilled from DeepSeek-R1's reasoning traces, under the MIT license. It thinks before it answers, which suits math and multi-step debugging but adds latency. It is an early-2025 model; for coding agents, Qwen3.8 27B above is the newer choice.
Verdict: Still a solid MIT-licensed reasoning model if you already run it.
ollama run deepseek-r1:32b
Mistral Small 3 24B
Mistral AIThe mistral-small:24b tag is Mistral Small 3 (24B Instruct 2501), the January 2025 release, and it is still Ollama's default for mistral-small. Ollama's page lists native function calling and JSON output, under Apache 2.0, which makes it a predictable backend for a local API server that needs structured responses. The Ollama build has a 32K context.
Verdict: Pick it for structured output, not for agentic coding.
ollama run mistral-small:24bSource: Ollama: mistral-small
Phi-4 14B
MicrosoftMicrosoft's 14B dense model is MIT licensed and fits a 16 GB laptop with room left for an editor. The Ollama build has a 16K context, far shorter than Gemma 4's, so treat it as the alternative when you want an MIT license rather than Apache 2.0.
Verdict: A license-driven alternative to Gemma 4 12B.
ollama run phi4:14bSource: microsoft/phi-4 on Hugging Face
Phi-4 Mini Reasoning
MicrosoftA 3.84B model from Microsoft fine-tuned for multi-step math reasoning on memory-constrained hardware, under the MIT license. At a 3.2 GB download with a 128K context it runs on almost anything, which makes it useful for experiments and embedded reasoning tasks rather than general chat.
Verdict: Good for experiments on low-memory machines.
ollama run phi4-mini-reasoning
Llama 4 Scout
MetaMeta's mixture-of-experts model lists a 10M-token context window on its model card. The 4-bit Ollama download (Q4_K_M) alone is 67 GB, so this is workstation hardware, not a desktop GPU.
Verdict: Only worth the memory if you need very long context locally.
ollama run llama4:scoutSource: Llama 4 Scout model card
COMPARISON TABLE
| # | Model | Params | Min RAM | Best For | License | Rating |
|---|---|---|---|---|---|---|
| 1 | | 27B dense | 24 GB VRAM or 32 GB unified (48 GB for long context) | Agentic coding on a single GPU | Apache 2.0 | 9.4/10 |
| 2 | | 12B dense, multimodal | 16 GB | The 16 GB laptop default | Apache 2.0 | 9.1/10 |
| 3 | | 25.2B total / 3.8B active (MoE) | 24 GB (Ollama build is 16-19 GB) | Fast responses with vision | Apache 2.0 | 9.0/10 |
| 4 | | 125B total / 6B active (MoE) | 32 GB RAM plus a 12 GB GPU (64 GB recommended) | The biggest model a gaming PC can run | Qwen Community License 1.0 | 8.9/10 |
| 5 | | 30.7B dense | 24 GB VRAM or 32 GB unified (Ollama build is 19-20 GB) | Highest Gemma 4 scores | Apache 2.0 | 8.8/10 |
| 6 | | 4.5B effective (8B with embeddings) | 8 GB (12-16 GB is comfortable) | 8 GB laptops and edge devices | Apache 2.0 | 8.6/10 |
| 7 | | 32B (distilled from DeepSeek-R1) | 24 GB (Ollama build is 20 GB) | Step-by-step reasoning | MIT | 8.4/10 |
| 8 | | 24B | 16 GB VRAM or 24 GB+ unified (Ollama build is 14 GB) | Function calling and JSON output | Apache 2.0 | 8.2/10 |
| 9 | Phi-4 14B | 14B | 16 GB (Ollama build is 9.1 GB) | MIT-licensed mid-size model | MIT | 8.1/10 |
| 10 | Phi-4 Mini Reasoning | 3.84B | 8 GB (Ollama build is 3.2 GB) | Reasoning on tiny hardware | MIT | 7.9/10 |
| 11 | | 109B total / 17B active (MoE, 16 experts) | 96 GB+ (Ollama Q4 build is 67 GB) | Very long context | Llama 4 Community License | 7.8/10 |
Previous generation
Qwen 3.5 and Gemma 3 led this list earlier in 2026. Qwen3.8 27B and the Gemma 4 family replace them here. If you already run them, they still work: the current Ollama tags are qwen3.5:9b and qwen3.5:27b for Qwen 3.5, and gemma3:4b and gemma3:27b for Gemma 3.
Llama 4 Maverick (a 245 GB download at 4-bit) and trillion-parameter open models are datacenter-scale rather than local. For open weights at any size, see the best open-source models.
FAQ
What is the best local AI model in 2026?
It depends on your memory: on a 16 GB laptop start with Gemma 4 12B, on a desktop with a 24 GB GPU or a 32 GB+ Mac start with Qwen3.8 27B, and with 48 GB or more run Qwen3.8 27B at 8-bit ('ollama run qwen3.8:27b-q8_0', a 30 GB build) for long-context agent work; Llama 4 Scout needs 96 GB+ and earns it only for its 10M-token context. Qwen3.8 27B scores 61.7 on SWE-bench Pro in Qwen's model card, ahead of Opus 4.6 Max at 53.4, and Google says Gemma 4 12B scores near its 26B MoE at less than half the memory. Both are Apache 2.0 and install with one Ollama command.
Can I run AI models on my MacBook?
Yes. An 8 GB MacBook runs Gemma 4 E4B (6.6 GB at 4-bit), and the open-source TurboFieldfare engine runs Gemma 4 26B in about 2 GB of RAM at 5.1-6.3 tokens per second on an 8 GB M2 MacBook Air. A 16 GB MacBook runs Gemma 4 12B, which Google built for 16 GB laptops. A 32-48 GB Mac runs Qwen3.8 27B; one Hacker News commenter reported 15 tokens per second in power-save mode and 30 in performance mode on a 48 GB M5 Max MacBook.
How do I install local AI models?
Install Ollama from ollama.com, then run one command such as 'ollama run gemma4:12b' or 'ollama run qwen3.8:27b'. Ollama downloads a quantized build (4-bit for most models on this page), uses your GPU or Apple Silicon automatically, and serves an OpenAI-compatible API on your machine. LM Studio is the desktop GUI alternative, and Strata is the installer for Qwen3.8-Flash-Next on Windows and Linux gaming PCs.
How much RAM do I need to run a local LLM?
8 GB runs a 4B-class model such as Gemma 4 E4B, 16 GB runs Gemma 4 12B or Phi-4 14B, 24-32 GB runs 27B-31B models such as Qwen3.8 27B and Gemma 4 31B (18-20 GB downloads at 4-bit), and 64 GB of RAM plus a 12 GB GPU runs the 125B Qwen3.8-Flash-Next through Strata. Leave several GB free, because long prompts grow the KV cache.
Are local AI models as good as ChatGPT or Claude?
For agentic coding loops the gap is small on vendor benchmarks: Qwen3.8 27B scores 61.7 on SWE-bench Pro against 53.4 for Opus 4.6 Max in Qwen's own evaluation. Frontier cloud models still lead on deep reasoning, for example 40.0 against 30.8 on HLE in the same table, and on the hardest terminal and repo-scale tasks. Treat vendor numbers as a starting point and test on your own work.
Go deeper
- Qwen3.8 27B vs Opus 4.6 Max - benchmarks, hardware math and when to stay on a frontier API
- Run Qwen3.8-Flash-Next on a gaming PC with Strata - the 125B MoE on a 12 GB GPU, speeds by quant, and the license catch
- Gemma 4 developer guide - Ollama, vLLM and fine-tuning workflows for the Gemma 4 family
- Gemma 4 26B in 2 GB of RAM on any M-series Mac - how TurboFieldfare streams experts from the SSD
- Bonsai 27B on a phone - PrismML's 1-bit compression of a 27B model
- LM Studio Bionic - a local-first agent app for open models, and its closed-source catch
- Ollama vs LM Studio vs vLLM vs llama.cpp - picking a runtime for a local coding agent
- The best local coding LLMs in 2026 - the coding-only shortlist, plus cost and compliance math
Sources
- Qwen3.8-27B model card (benchmarks, context, thinking mode)
- Qwen3.8-Flash-Next model card
- Qwen Community License 1.0 (Flash-Next weights)
- Strata README (requirements and RTX 5070 speeds)
- Gemma 4 model card (sizes, context, LiveCodeBench)
- Google: Introducing Gemma 4 12B (June 3, 2026)
- TurboFieldfare repository (Gemma 4 26B in about 2 GB of RAM)
- Llama 4 Scout model card
- Ollama library (tags and download sizes for every command on this page)
All sources checked October 8, 2026. Benchmark scores are the vendors' own unless stated, and speeds come from the linked READMEs or community reports, not from our own runs. Ratings are our editorial scores, not user reviews.
Getting Started
The easiest way to run local models is with Ollama. Install it, then run any model with a single command. Ollama handles downloading, quantization, and GPU acceleration automatically. It also provides an OpenAI-compatible API, so you can use local models with any tool that supports OpenAI. If you want a GUI or need to choose a runtime for a coding agent, our local runtime comparison covers Ollama, LM Studio, vLLM and llama.cpp.
Ollama pulls quantized builds by default to cut memory use: Gemma 4 12B is a 7.7-8.0 GB download and the 27B-32B models on this page are 18-20 GB, before the KV cache your prompts add. How much quality quantization costs varies by model (one Hacker News tester found Qwen3.8 27B failed a hard private benchmark once its KV cache was quantized, while Gemma 4 held up), so test the build on your own work before standardizing on it.
Want to see these models compared?
I run benchmarks and real-world tests on local models and post the results on the channel. See how these models actually perform on coding, reasoning, and creative tasks.
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