AI Model Recommender
Not sure which model to run? Select your task and hardware — we'll recommend the best local AI models with install commands, VRAM requirements, and quality scores. Works for Ollama, llama.cpp, and any local inference engine.
Find Your Perfect Model
Top 5 Models for General Chat (24 GB (RTX 4090 / Mac 24GB))
Near-GPT-4 quality locally
ollama run qwen2.5:32bBest value at 14B
ollama run qwen2.5:14bExcellent for general tasks
ollama run gemma2:9bBest balanced 8B model
ollama run llama3.1:8bUnderstand images locally
ollama run llama3.2-vision:11bUpgrade to unlock these models
Scores at Q4_K_M quantization. See our VRAM Calculator for exact memory requirements, or our GPU comparison for hardware recommendations.
How We Score Models
Our recommendation engine combines benchmark data, community feedback, and real-world testing across seven dimensions:
Benchmark Sources
- Coding: HumanEval, SWE-bench Verified, Aider polyglot
- Reasoning: MATH, GPQA Diamond, ARC-AGI
- General: MMLU, LMArena ELO
- Creative: AlpacaEval, human preference ratings
Practical Factors
- VRAM efficiency: Q4_K_M quantization size from GGUF files
- Speed: Tokens/sec on RTX 4090, scaled for other hardware
- Ecosystem: Ollama availability, community support, documentation
- License: All recommended models are free for commercial use
Key principle: A model that fits entirely in your GPU VRAM will always outperform a larger model that spills to system RAM. Our recommendations prioritize models that run at full speed on your hardware. Use the VRAM Calculator to verify exact memory requirements for any model and quantization format.
Quick Guide: Model Size vs Quality
| Size | VRAM (Q4) | Quality Level | Best For | Hardware |
|---|---|---|---|---|
| 1-3B | 1-3 GB | Basic | Simple Q&A, autocomplete, edge devices | Any 4GB device |
| 7-9B | 5-6 GB | Good | Daily chat, coding assistance, RAG | 8GB GPU or Mac |
| 14B | 9-10 GB | Very Good | Complex reasoning, debugging, analysis | RTX 3060 12GB |
| 32B | 20-22 GB | Near-GPT-4 | Professional coding, writing, planning | RTX 4090 or 32GB Mac |
| 70B | 40-42 GB | GPT-4 Class | Expert-level tasks, research, production | 64GB Mac or 2x GPU |
| 100B+ MoE | 50-60 GB | Frontier | Maximum quality, long context, vision | 128GB Mac or 3x GPU |
VRAM at Q4_K_M quantization with 4K context. Longer contexts add 0.5-4 GB. See hardware guide for detailed requirements.
Frequently Asked Questions
What is the best local AI model for coding in 2026?
For coding, Qwen 2.5 Coder 32B leads with 92.7% on HumanEval — rivaling GPT-4o while running entirely on your machine. It needs 22 GB VRAM (RTX 4090 or 32GB Mac). For smaller setups, Qwen 2.5 Coder 7B (5 GB VRAM) is the best coding model under 8GB, scoring 82% on HumanEval. DeepSeek R1 14B (9.5 GB) excels at complex debugging with its chain-of-thought reasoning.
Which LLM runs best on 8GB VRAM?
With 8GB VRAM, your best options are Llama 3.1 8B (5.5 GB, versatile all-rounder), Mistral 7B (5 GB, fast and reliable), Gemma 2 9B (6 GB, excellent reasoning), and Qwen 2.5 Coder 7B (5 GB, best small coding model). All run at full GPU speed on an RTX 3070, RTX 4060, or 8GB Mac. Use Q4_K_M quantization for the best quality-to-size ratio.
What model should I use for RAG (retrieval-augmented generation)?
For RAG, Qwen 2.5 32B (22 GB VRAM) provides the best balance of context understanding and factual accuracy. If limited on VRAM, Llama 3.1 8B (5.5 GB) handles RAG well thanks to its 128K context window. For maximum context, Llama 4 Scout supports up to 10M tokens. Pair any of these with ChromaDB or Qdrant for the vector database.
How do I choose between model sizes (7B, 14B, 32B, 70B)?
Model quality scales with size but with diminishing returns: 7B models handle 80% of tasks adequately, 14B models add noticeable improvement in reasoning and nuance, 32B models approach GPT-4 quality for most tasks, and 70B models match or exceed GPT-4. Always run the largest model your hardware can fit entirely in VRAM — a 14B model at full GPU speed beats a 70B model spilling to RAM.
Can I run AI models with just system RAM (no GPU)?
Yes, but much slower. CPU-only inference on a modern processor runs 7B models at 5-15 tokens/second (vs 100-200 tok/s on GPU). Apple Silicon Macs are the exception — their unified memory architecture lets M2/M3/M4 chips run models at near-GPU speed. A Mac Mini M4 with 16GB runs Qwen 2.5 14B at 30+ tok/s, which is very usable.
What is the best free AI model for creative writing?
Llama 3.3 70B produces the most natural, creative prose among open models — it handles fiction, marketing copy, poetry, and dialogue at near-GPT-4 level. Needs 42 GB VRAM or a 64GB Mac. For smaller setups, Gemma 2 9B (6 GB) and Qwen 2.5 32B (22 GB) both produce excellent creative output. All are free, open-weight models you can run privately via Ollama.
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Written by Pattanaik Ramswarup
AI Engineer & Dataset Architect | Creator of the 77,000 Training Dataset
I've personally trained over 50 AI models from scratch and spent 2,000+ hours optimizing local AI deployments. My 77K dataset project revolutionized how businesses approach AI training. Every guide on this site is based on real hands-on experience, not theory. I test everything on my own hardware before writing about it.
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