Best Local AI Models for 8GB VRAM
These open-weight models run comfortably on a GPU with 8 GB of VRAM at Q4 quantization, so a mid-range gaming card or a recent laptop is enough to run a real LLM locally.
Llama 3.1 8B
Meta · Llama 3.1 Community
Meta's 8B workhorse with a massive 128K context window. Punches well above its weight class for everyday coding and chat tasks, runs comfortably on any consumer GPU with 6 GB VRAM, and delivers near-70B quality on many benchmarks.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run llama3.1:8bLlama 3.2 3B
Meta · Llama 3.2 Community
The smallest production-quality Llama model, designed to run on laptops, Raspberry Pis, and other edge devices with minimal VRAM. Response latency is sub-second on modern CPUs, making it ideal for real-time assistants and local automation scripts.
Min VRAM
2 GB
Context
128K
Run with Ollama
ollama run llama3.2:3bQwen3 8B
Alibaba · Apache 2.0
One of the most-downloaded mid-size AI models on HuggingFace in 2026, with tens of millions of downloads. Qwen3 8B outperforms the previous Qwen2.5 14B on most benchmarks, supports hybrid thinking mode and tool calling, and runs on any GPU with 6 GB VRAM. Apache 2.0 license — fully commercial.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run qwen3:8bMistral 7B v0.3
Mistral AI · Apache 2.0
The model that proved 7B parameters can match much larger models on reasoning tasks when trained carefully. Apache 2.0 licensed, so it is fully free to use commercially with no restrictions.
Min VRAM
4 GB
Context
32K
Run with Ollama
ollama run mistral:7bMistral Nemo 12B
Mistral AI · Apache 2.0
Built in partnership with NVIDIA and trained on a broad multilingual corpus, Nemo is Mistral's best model for non-English tasks across European and Asian languages. The 128K context window makes it practical for processing long documents locally.
Min VRAM
7 GB
Context
128K
Run with Ollama
ollama run mistral-nemo:12bGemma 4 12B
Google · Apache 2.0
Google's latest Gemma generation doubles the context window to 256K tokens and — a major shift from earlier Gemma releases — ships under the Apache 2.0 license. The 12B model is one of the most actively fine-tuned coding bases on HuggingFace in 2026, with hundreds of community GGUF variants. Runs on any GPU with 10 GB VRAM.
Min VRAM
8 GB
Context
256K
Run with Ollama
ollama run gemma4:12bGemma 3 4B
Google · Gemma Terms of Use
Google's compact Gemma 3 model brings the quality of a much larger system into a 4B package that runs on integrated graphics. A strong first choice for anyone wanting a capable, low-power model without needing a discrete GPU.
Min VRAM
3 GB
Context
128K
Run with Ollama
ollama run gemma3:4bPhi-4 14B
Microsoft · MIT
Microsoft's research-driven Phi-4 focuses on synthetic high-quality training data, producing a 14B model that outperforms much larger models on coding and mathematical reasoning benchmarks. MIT licensed with zero usage restrictions for commercial projects.
Min VRAM
8 GB
Context
16K
Run with Ollama
ollama run phi4:14bDeepSeek R1 Distill 8B
DeepSeek · MIT
A distilled version of DeepSeek's R1 reasoning model that inherits chain-of-thought problem-solving capabilities in a compact 8B package. One of the few small models that can reliably work through multi-step math and logic problems without external tooling.
Min VRAM
5 GB
Context
128K
Run with Ollama
ollama run deepseek-r1:8bQwen 2.5 Coder 7B
Alibaba · Apache 2.0
A coding-specialist model fine-tuned on a massive corpus of source code across 40+ programming languages, delivering autocomplete and generation quality that rivals dedicated IDE tools. At 7B it is fast enough for real-time code suggestions on a single consumer GPU.
Min VRAM
4 GB
Context
128K
Run with Ollama
ollama run qwen2.5-coder:7bWant to mix in more filters?
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