Quick Answer
With 6 GB VRAM, Llama 3 8B Q4_K_M is the top pick at ~5.5 GB. Phi-4 Q4_K_M and Mistral 7B Q4_K_S are solid alternatives.
Updated: 2026-05
Key Takeaways
As of May 2026, 6 GB VRAM covers two very different hardware classes: budget Windows laptops (RTX 3050/4050) and any MacBook with 16 GB unified memory. Performance differs by 30–50% between them — the Mac runs Llama 3 8B Q4_K_M at ~25 tok/s thanks to unified memory bandwidth, while the Windows discrete GPU runs it at ~18 tok/s due to PCIe transfer overhead.
All three models run with Ollama out of the box. Speed figures below assume a 2048-token context window. Extending to 4096 tokens adds ~1 GB — still within 6 GB for Phi-4 and Mistral.
| Model | VRAM | Best For |
|---|---|---|
| Llama 3 8B Q4_K_M | 5.5 GB | General chat, coding |
| Phi-4 Q4_K_M | 5.0 GB | Instructions, reasoning |
| Mistral 7B Q4_K_S | 4.5 GB | Speed-first tasks |
On Windows, the RTX 3050 6 GB and RTX 4050 6 GB are the two main GPUs at this tier. Both run Ollama via CUDA with nearly identical performance — the newer RTX 4050 is about 10% faster per watt but not meaningfully faster in practice.
On macOS, any MacBook with 16 GB unified memory has approximately 6 GB available for the GPU workload. Unified memory eliminates the PCIe bandwidth bottleneck that limits discrete GPU cards, so macOS performance is often equal to or better than a discrete RTX 3050.
Upgrading from 6 GB to 8 GB unlocks Q5_K_M quantization on 7–8B models (+3% quality) and faster context windows. For 12 GB options and 14B models, see best Ollama models for RTX 3060 12 GB. For the full VRAM reference, see how much VRAM a local LLM needs.
6 GB is the smallest VRAM where a local LLM competes with cloud models on everyday tasks. Below 6 GB, you are limited to small models that struggle on coding or long-form reasoning. At 6 GB, Llama 3 8B Q4_K_M is fully unlocked — the same model that powers many production AI features. To step up to 14B models, see the 12 GB tier picks.
--num-ctx 2048) or choose Phi-4 Q4_K_M instead.