Best Local LLM for 16GB VRAM
16 GB of VRAM unlocks Gemma 3 27B, the largest Gemma 3 model, which needs about 15 GB at Q4 and delivers instruction following that rivals models twice its size. OpenAI's gpt-oss-20b and every smaller model in the catalog also fit, most of them with plenty of headroom.
Model data last verified on September 16, 2026. All VRAM figures are for the Q4 quantization Ollama serves by default.
Every model that fits 16 GB of VRAM
12 of the 17 local LLMs in our verified catalog fit this budget at Q4, sorted with the largest fit first. Click any model for its full spec sheet, hardware cross-reference and run commands.
| Model | Params | Quant | Min VRAM | Context | Fit |
|---|---|---|---|---|---|
| Gemma 3 27B | 27B | Q4 | 15 GB | 128K | 1 GB free |
| OpenAI gpt-oss-20bHistoric first | 20B total / 3.6B active (MoE) | Q4 | 12 GB | 128K | 4 GB free |
| Gemma 4 12BBest for fine-tuning | 12B | Q4 | 8 GB | 256K | 8 GB free |
| Phi-4 14BBest small coder | 14B | Q4 | 8 GB | 16K | 8 GB free |
| Mistral Nemo 12B | 12B | Q4 | 7 GB | 128K | 9 GB free |
| Llama 3.1 8B | 8B | Q4 | 5 GB | 128K | 11 GB free |
| Qwen3 8BMost popular | 8B | Q4 | 5 GB | 128K | 11 GB free |
| DeepSeek R1 Distill 8BMost private | 8B | Q4 | 5 GB | 128K | 11 GB free |
| Mistral 7B v0.3 | 7B | Q4 | 4 GB | 32K | 12 GB free |
| Qwen 2.5 Coder 7B | 7B | Q4 | 4 GB | 128K | 12 GB free |
| Gemma 3 4B | 4B | Q4 | 3 GB | 128K | 13 GB free |
| Llama 3.2 3BFastest | 3B | Q4 | 2 GB | 128K | 14 GB free |
Run the top picks with Ollama
One command each. Ollama pulls the Q4_K_M build by default and exposes an OpenAI-compatible endpoint at localhost:11434/v1.
ollama run gemma3:27bollama run gpt-oss:20bollama run gemma4:12bHardware that gives you 16 GB
Tracked cards and Macs with at least 16 GB of fast memory, cheapest first.
Frequently asked questions
What is the best local LLM for 16GB of VRAM?
Gemma 3 27B. It needs about 15 GB at Q4, so it just fits a 16 GB card, and it delivers instruction-following quality that rivals models twice its size. If you want more headroom, gpt-oss-20b needs about 12 GB and generates faster thanks to its mixture-of-experts design.
Which 16GB GPU should I buy for local AI?
The RTX 5070 Ti 16GB (around $999) is the best value in NVIDIA's current lineup, with GDDR7 bandwidth and a 300W draw. On a budget, the previous-gen RTX 4060 Ti 16GB (around $424) offers the most VRAM per dollar, though its narrower bus makes large models generate slower.
Can 16GB of VRAM run a 70B model?
Not at usable quality. Llama 3.3 70B needs about 48 GB at Q4. On a 16 GB card a 70B model would need extreme quantization plus CPU offload, and both speed and quality suffer. The 20B to 27B class is the practical ceiling.
Can 16GB run Ornith 1.0 35B?
No. Ornith 1.0 35B needs about 20 GB at Q4, so it does not fit in 16 GB without offload. It is the headline pick of the 24 GB tier.
Is there a newer model than Gemma 3 27B for 16GB VRAM?
Not one that fits. Alibaba released Qwen3.8-27B on August 14, 2026 with native image and video understanding and a 256K context window, but its Q4 build needs about 18 GB, just over a 16 GB card. It is the headline new pick of the 24 GB tier instead. Within 16 GB, Gemma 3 27B and gpt-oss-20b remain the strongest fits.
Different budget or use case?
The faceted model browser combines VRAM, family, license and task filters over the same verified catalog.