Yes — with 6.1 GB to spare
Gemma 3 12B at Q4_K_M fits your Radeon RX 9070 entirely on the GPU at 8K context, at an estimated 57 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 | — | ~3.3 | Reference | 9.7 GB over |
| Q8_0 | 12.1 GB | 13.5 GB | 22K | 32 | −0.1% ppl | Long context |
| Q6_K | 9.3 GB | 10.7 GB | 66K | 42 | −0.4% ppl | Long context |
| Q5_K_M | 8.1 GB | 9.5 GB | 86K | 48 | −0.8% ppl | Long context |
| Q4_K_M | 6.9 GB | 8.3 GB | 106K | 57 | −1.9% ppl | Recommended |
| Q3_K_M | 5.6 GB | 7.0 GB | 126K | 70 | −5.4% ppl | Long context |
| Q2_K | 4.8 GB | 6.2 GB | 128K | 82 | −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.