Best Local AI Models
Models evaluated by hardware efficiency, VRAM requirements (16GB to 80GB), quantization fidelity, and offline data privacy.
Top Local & Self-Hosted Foundation Models
Ranked by local execution feasibility and benchmark performance.
DeepSeek-R1
Open-weights frontier reasoning model trained via large-scale reinforcement learning without supervised cold start.
64k tokens
671B (37B active)
$0.55 / $2.19 / MTok
Codestral 2501
Mistral AI's dedicated code generation model with 256k context window and fill-in-the-middle (FIM) capabilities.
256k tokens
22B
$0.30 / $0.90 / MTok
DeepSeek-V3
Frontier open-weights 671B MoE base and chat model with multi-head latent attention (MLA) and dual-pipe training.
64k tokens
671B (37B active)
$0.14 / $0.28 / MTok
Llama 3.3 70B Instruct
Meta's open-weights 70B flagship matching Llama 3.1 405B capabilities on industry benchmarks at 1/5th the compute.
128k tokens
70B
$0.12 / $0.30 / MTok
Qwen 2.5 Coder 32B Instruct
Alibaba's open-weights code generation specialist matching GPT-4o on coding benchmarks while fitting on a single GPU.
128k tokens
32.5B
$0.00 / $0.00 / MTok
Qwen 2.5 72B Instruct
Alibaba's flagship open foundation model with world-class multilingual, mathematical, and coding capabilities.
128k tokens
72.7B
$0.00 / $0.00 / MTok
Mistral Large 2 (2407)
Mistral AI's flagship 123B model specialized in multilingual reasoning, precision code generation, and agentic tool use.
128k tokens
123B
$2.00 / $6.00 / MTok
Llama 3.1 405B Instruct
Meta's largest open foundation model with 405 billion dense parameters, rivaling leading closed frontier models.
128k tokens
405B
$1.79 / $1.79 / MTok
Quick Start: Run Locally via Ollama
Install Ollama and pull any of the verified models with a single terminal command:
# Run DeepSeek-R1 (32B / 70B):
ollama run deepseek-r1:32b
# Run Qwen 2.5 Coder (32B):
ollama run qwen2.5-coder:32b
Local Deployment FAQ
How much VRAM is needed for running local AI models?
A 32B model (like Qwen 2.5 Coder 32B) runs smoothly in 4-bit (Q4_K_M) on a single 24GB GPU (RTX 3090/4090) or 32GB Mac. A 70B model (like Llama 3.3 70B) requires ~40GB–48GB VRAM (dual 24GB GPUs or 64GB Mac).
What is the fastest tool for running models locally?
Ollama and LM Studio provide the easiest setup for Mac/Windows, while vLLM and TensorRT-LLM offer maximum multi-GPU throughput for enterprise servers.
Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.