What are the best local AI models in 2026?

Top local AI models in 2026: Llama 3.3 70B for general chat (86.0 MMLU CoT per Meta's model card, 42GB VRAM), Qwen 2.5 32B for the quality-to-VRAM sweet spot (24GB), Qwen3-Coder-Next for coding (70.6% SWE-bench, 46GB), DeepSeek R1 for reasoning, and Llama 3.1 8B as the best all-rounder for limited hardware (5GB VRAM). All run free on your own hardware via Ollama.

How much VRAM do I need for local AI models?

Rule of thumb: multiply parameters by 0.6 for Q4 quantization. 7B models need ~5GB VRAM (RTX 3060), 13B needs ~8GB, 32B needs ~20GB (RTX 4090), 70B needs ~42GB (RTX 5090 or dual GPUs). Apple Silicon Macs can use unified memory. An RTX 5080 (16GB, $999) handles all models up to 14B. An RTX 5090 (32GB, $1,999) handles up to 70B quantized.

Are local AI models better than cloud APIs?

Local AI models have no per-token fees once you own the hardware (whether that is cheaper depends on your volume and electricity rate), offer complete data privacy, have no rate limits, and work offline. However, cloud APIs (Claude, GPT-5) still outperform local models on complex reasoning tasks. Best approach: use local models for daily coding, drafting, and privacy-sensitive work; use cloud APIs for complex multi-step tasks. Many developers use both.

How do I install and run local AI models?

The easiest way is Ollama: install from ollama.com, then run "ollama pull llama3.1:8b" followed by "ollama run llama3.1:8b". For a GUI, add Open WebUI. For coding, use Continue.dev with Ollama. All models in this directory include Ollama commands. See our Best Ollama Models guide for recommended models by task and hardware.

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Local AI Models Directory: Every Model Compared

Updated: November 2, 2026

The complete, browsable list of local AI models — 143+ LLMs compared side by side. Sort and filter by RAM/VRAM, benchmark score, vendor, modality, context length and license, and copy a ready-to-run Ollama install command for any model in the table below.

Local models

Local AI Models Directory

Browse 143+ vendor-vetted local AI models with specs, context windows, leaderboard rankings, and download links. Use the filters below to pinpoint the right assistant, coder, or multimodal model for your hardware.

Compare by vendor, modality, context length, license, and recommended hardware. Need help sizing your rig? Read the hardware guide or walk through the Windows installation checklist before downloading your first model.

Directory refreshed 2026-11-03

Average context length: 164K tokens • Total vendors: 2 • Benchmarked models: 2/2 • Top modality: text

View comparison dashboard Ollama troubleshooting

Models at a glance

Avg. parameter counts by vendor: Meta: 8.0B

Top benchmarked models

Llama 3.1 8B Advanced Local Deployment Guide82.35
Claude 3 Haiku High-Velocity Deployment Blueprint80.30

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📚 Research Sources & Benchmarks

Academic Research Papers

Benchmark Platforms

💡 Where the numbers come from: Local AI Master does not run these benchmarks. The MMLU, HumanEval and MGSM scores in the dashboard below are vendor-reported figures as compiled in OpenAI's simple-evals repository (read Nov 2, 2026), except DeepSeek Coder V2, which comes from DeepSeek's own README. Speeds are memory-bandwidth ceilings (vendor bandwidth ÷ Ollama file size), not measurements.

📊 Comprehensive Model Comparison Dashboard

Reference scores for the 2024 flagship generation, the hardware each model class needs, and what drives running costs. Scores are vendor-reported (sources above); hardware figures are vendor specs and Ollama file sizes.

🏆 Performance Leaders (Vendor-Reported Scores)

Overall Performance (MMLU)

Llama 3.1 405B

Meta • 128K

88.6

simple-evals (reported)

Claude 3.5 Sonnet

Anthropic • 200K

88.3

simple-evals (reported)

GPT-4 Turbo (2024-04-09)

OpenAI • 128K

86.7

simple-evals

Gemini 1.5 Pro

Google • 1M

81.9

simple-evals (reported)

Coding Performance (HumanEval)

Claude 3.5 Sonnet

simple-evals (reported)

92%

Anthropic

DeepSeek Coder V2 236B

DeepSeek README

90.2%

DeepSeek

Llama 3.1 405B

simple-evals (reported)

89%

Math & Reasoning (MGSM)

Claude 3.5 Sonnet

simple-evals (reported)

91.6%

Anthropic

Llama 3.1 405B

simple-evals (reported)

91.6%

💰 Hardware Requirements & Running Costs

Hardware Requirements (Vendor Specs + Ollama File Sizes)

Consumer (24GB VRAM)

Recommended Models:

Llama 3.1 8BMistral 7BPhi-3 MiniGemma 2B

GPU Required:RTX 3090 / RTX 4090

Hardware Cost:Current prices on /hardware

Performance:~190-205 tok/s ceiling (Llama 3.1 8B Q4)

VRAM Needed:4.9 GB (llama3.1:8b, Q4)

Professional (48GB VRAM)

Recommended Models:

Llama 3.1 70BMixtral 8x7BDeepSeek Coder V2 16B

GPU Required:RTX 6000 Ada / 2x RTX 4090

Hardware Cost:Check current listings

Performance:~22-24 tok/s ceiling (Llama 3.1 70B Q4)

VRAM Needed:42.5 GB (llama3.1:70b, Q4)

Enterprise (80GB+ VRAM)

Recommended Models:

Llama 3.1 405B

GPU Required:4x H100 80GB / A100 80GB

Hardware Cost:Usually rented by the hour

Performance:Not quoted (multi-GPU, depends on parallelism)

VRAM Needed:243 GB (llama3.1:405b, Q4)

What Drives Monthly Running Costs

There is no honest single monthly figure: electricity is GPU board power × hours under load × your rate, plus the hardware itself. Board power below is the vendor spec. Put your own usage and electricity rate into the AI cost calculator to compare against API pricing.

Llama 3.1 8B

4.9 GB at Q4

Hardware:RTX 4090

vs API Cost:Depends on your volume

Power Usage:450W (NVIDIA spec)

Investment:Run your numbers in the calculator

Llama 3.1 70B

42.5 GB at Q4

Hardware:2x RTX 4090

vs API Cost:Depends on your volume

Power Usage:900W (2 × 450W)

Investment:Run your numbers in the calculator

Mixtral 8x7B

26.4 GB at Q4

Hardware:RTX 6000 Ada

vs API Cost:Depends on your volume

Power Usage:300W (NVIDIA spec)

Investment:Run your numbers in the calculator

Llama 3.1 405B

243 GB at Q4

Hardware:8x H100 80GB

vs API Cost:Depends on your volume

Power Usage:Up to 5.6kW (8 × 700W SXM)

Investment:Usually rented, not bought

🎯 Use Case Recommendations

Content Creation

Long-form drafting and editing. MMLU is a general-knowledge score, shown as context only.

Claude 3.5 Sonnet

Long-form content • MMLU: 88.3

Cloud API

Per-token pricing

Llama 3.1 70B

Creative writing • MMLU: 82.0

Runs locally

42.5 GB at Q4

GPT-4 Turbo

Marketing copy • MMLU: 86.7

Cloud API

Per-token pricing

Business Applications

Optimized for customer service, data analysis, and business intelligence tasks.

GPT-4 Turbo

Data analysis • 89.6% MGSM

Cloud API

Per-token pricing

Claude 3.5 Sonnet

Business logic • 91.6% MGSM

Cloud API

Per-token pricing

Llama 3.1 405B

Complex reasoning • 91.6% MGSM

Self-hosted

243 GB at Q4

Development & Technical

HumanEval scores are vendor-reported (sources above).

Claude 3.5 Sonnet

Code generation • 92.0% HumanEval

Cloud API

Per-token pricing

DeepSeek Coder V2 236B

Programming languages • 90.2% HumanEval

Self-hosted

132.9 GB at Q4

GPT-4 Turbo

Debugging & analysis • 88.2% HumanEval

Cloud API

Per-token pricing

📈 Directory Stats & Research Sources

143

Models in This Directory

Vendors

2/2

With Published Benchmarks

text

Most Common Modality

Research Sources:OpenAI simple-evals,DeepSeek-Coder-V2 README,Ollama library (file sizes),Stanford HELM,HuggingFace Leaderboard,Anthropic Documentation,Meta Research,Google DeepMind,Mistral AI

Last Updated: November 2, 2026. Scores are vendor-reported (not run by Local AI Master); file sizes read from the Ollama registry; GPU bandwidth and power from NVIDIA spec pages (RTX 3090 936 GB/s, RTX 4090 1,008 GB/s and 450W, RTX 6000 Ada 960 GB/s and 300W).

📖 In-Depth Model Guides

Deep-dive technical analyses with benchmarks, deployment guides, and use case recommendations.

🆕 Newest model guides

Nemotron 3.5 Lightning - 30B agent model on 32GB of RAM
Mage-VL - video understanding on 8GB VRAM
Unlimited-OCR - whole-PDF parsing on 8GB VRAM

🐍 CodeLlama Series

CodeLlama 7B - Multi-language coding
CodeLlama Python 7B - Lightweight Python assistant

💎 Google Gemma Series

Gemma 2 27B - High performance
Gemma 7B - Balanced efficiency
Gemma 2B - Edge deployment

☁️ Cloud & API Models

GPT-5.6 - OpenAI flagship, pricing and local options
Best Claude Model for Coding - Sonnet 5, Opus 4.8 and Fable 5 compared
GPT-OSS 20B / 120B - OpenAI's open-weight alternative you can run locally

💡 Need help choosing? Check our model selection guide or compare Llama vs Mistral vs CodeLlama for coding tasks.

Related Guides

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View All Local AI Guides

Topics in this guide

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