Yes — with 1.5 GB to spare
Gemma 4 12B at Q4_K_M fits your GeForce GTX 1080 Ti entirely on the GPU at 8K context, at an estimated 43 tokens per second. Past 32K 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 · 6.7 GB
Apache 2.0
Released 29 May 2026
Vision
The "unified" Gemma 4: text, image and audio in one 12B that fits a 12 GB card at Q4. 140+ languages.
The VRAM budget
weights 6.7 GB
Weights 6.7 GB
KV cache @ 8K 0.81 GB
Runtime overhead 0.6 GB
Free 1.5 GB of 9.7 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| F16 | 22.4 GB | 23.8 GB | — | ~2.4 | Reference | 14.1 GB over |
| Q8_0 | 11.9 GB | 13.3 GB | — | ~7.9 | −0.1% ppl | 3.6 GB over |
| Q6_K | 9.2 GB | 10.6 GB | — | ~19 | −0.4% ppl | 0.9 GB over |
| Q5_K_M | 7.9 GB | 9.3 GB | 13K | 37 | −0.8% ppl | Fits |
| Q4_K_M | 6.7 GB | 8.2 GB | 32K | 43 | −1.9% ppl | Recommended |
| Q3_K_M | 5.5 GB | 6.9 GB | 53K | 54 | −5.4% ppl | Long context |
| Q2_K | 4.7 GB | 6.1 GB | 65K | 63 | −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, 8 of 48 layers global), which is why its cache barely grows with context.
How to run it
$ ollama pull gemma4:12b $ OLLAMA_CONTEXT_LENGTH=8192 \ ollama run gemma4:12b
The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.
01Download is 6.7 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 32K context on this card.