Yes — with 6.1 GB to spare
Gemma 3 12B at Q4_K_M fits your GeForce RTX 4060 Ti 16 GB entirely on the GPU at 8K context, at an estimated 25 tokens per second. Past 106K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.
Fully on GPU
8K context
Q4_K_M · 6.9 GB
Gemma Terms of Use
Released Mar 2025
Vision
Strong multilingual chat with images, sized for 12–16 GB cards. Gemma 4 12B is the same size and better.
The VRAM budget
weights 6.9 GB
Weights 6.9 GB
KV cache @ 8K 0.81 GB
Runtime overhead 0.6 GB
Free 6.1 GB of 14.4 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| F16 | 22.7 GB | 24.1 GB | — | ~2.9 | Reference | 9.7 GB over |
| Q8_0 | 12.1 GB | 13.5 GB | 22K | 14 | −0.1% ppl | Long context |
| Q6_K | 9.3 GB | 10.7 GB | 66K | 19 | −0.4% ppl | Long context |
| Q5_K_M | 8.1 GB | 9.5 GB | 86K | 22 | −0.8% ppl | Long context |
| Q4_K_M | 6.9 GB | 8.3 GB | 106K | 25 | −1.9% ppl | Recommended |
| Q3_K_M | 5.6 GB | 7.0 GB | 126K | 31 | −5.4% ppl | Long context |
| Q2_K | 4.8 GB | 6.2 GB | 128K | 37 | −15% ppl | Long context |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model interleaves sliding-window layers (1024 tokens, 1 global in 6), which is why its cache barely grows with context.
How to run it
$ ollama pull gemma3:12b $ OLLAMA_CONTEXT_LENGTH=8192 \ ollama run gemma3:12b
The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.
01Download is 6.9 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.
03There is room to go to 106K context on this card.