Best LLM for 16GB VRAM (16GB VRAM)
What a 16GB GPU can really run — up to 14B at full quality
The best local LLM for 16GB VRAM in 2026 is Qwen 3 14B — ~9GB at Q4_K_M and ~35 tokens/sec on an RTX 4080, leaving room for a long context. Phi-4 14B is the best for STEM/reasoning, Gemma 3 12B the best multilingual, and DeepSeek-R1 14B the best chain-of-thought. 16GB runs any 14B at full Q4 quality with a generous context; a 32B model needs 24GB.
Models that fit in 16GB VRAM
Tested reference: RTX 4080 / RTX 4060 Ti 16GB. Figures are for Q4_K_M with a modest context window.
| Model | Size | Build | VRAM used | Speed |
|---|---|---|---|---|
| WINNERQwen 3 14B Best overall at 16GB — reasoning, coding and 128K context with room to spare. ollama pull qwen3:14b | 14B | Q4_K_M | ~9.0GB | ~35 tok/s |
| Phi-4 14B Microsoft’s STEM/reasoning specialist — punches above its size on math. ollama pull phi4 | 14B | Q4_K_M | ~9.0GB | ~34 tok/s |
| Gemma 3 12B Best multilingual; strong writing quality. ollama pull gemma3:12b | 12B | Q4_K_M | ~8.0GB | ~38 tok/s |
| DeepSeek-R1 14B Distilled reasoning model — shows its chain-of-thought; great for hard problems. ollama pull deepseek-r1:14b | 14B | Q4_K_M | ~9.0GB | ~33 tok/s |
| Qwen 2.5 32B (Q3) A 32B *just* squeezes in at Q3 with short context — more knowledge, but slower and lower fidelity than a 14B at Q4. ollama pull qwen2.5:32b-instruct-q3_K_S | 32B | Q3_K_S | ~15GB | ~14 tok/s |
What won't fit in 16GB
- ✗Qwen 2.5 32B (Q4) (needs ~20GB) — Full-quality 32B needs 24GB.
- ✗Llama 3.3 70B (needs ~40GB) — Needs 48GB (or two 24GB cards).
How to fit more in 16GB
- →At 16GB you can run a 14B at Q5_K_M for slightly better quality if you keep context ≤8K.
- →Prefer a 14B at Q4 over a 32B at Q3 — the higher-fidelity smaller model usually wins on real tasks and is 2× faster.
- →Enable flash-attention (OLLAMA_FLASH_ATTENTION=1) to stretch the context window further.
Quick start
curl -fsSL https://ollama.com/install.sh | shollama run qwen3:14bGo from “it runs” to actually building
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Start learning free →Frequently asked questions
Can 16GB VRAM run a 32B model?
Only at Q3 with a short context, at ~14 tok/s. It works but a 14B at Q4 is faster and usually higher quality on real tasks. For comfortable 32B, use 24GB.
Can 16GB VRAM run a 70B model?
No. A 70B model at Q4 needs ~40GB. On 16GB you would offload most layers to system RAM and drop to a few tok/s. Stick to 14B locally, or use a 70B via API.
What GPU has 16GB VRAM for AI?
The RTX 4080, RTX 4060 Ti 16GB, RTX 4070 Ti Super, and RTX 5070 Ti. On Apple Silicon, a 16GB unified-memory Mac behaves similarly for models up to ~14B.
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