Can RTX 3060 12GB (32GB RAM) run Gemma 4 12B?
Measured results for Gemma 4 12B (11.96B dense) on RTX 3060 12GB (32GB RAM), plus what fits and the commands to run it.
Short answer
Yes. Gemma 4 12B Q4_K_M fits fully on RTX 3060 12GB (32GB RAM) at 16k context, using 7.7 of 11.0 GB usable, and generates about 39.7 tokens/s in our own run. As the context fills it slows down: 36.8 tok/s with 16k tokens already in context (93% of the empty-context speed), measured. For Q6_K, our bandwidth-only estimate was ~20 tok/s against a measured median of ~29 (30% too pessimistic).
Measured generation speed by quantization
| Quant | Generation tok/s | Range | Prompt tok/s | Runs | Origin |
|---|---|---|---|---|---|
| Q4 class (median) | 39.7 | 39.7–39.7 | 1277 | 1 | includes our runs |
| Q4_K_M (our run, depth 0) | 39.7 | 3 repeats | 1277 | 3 | own measurement |
Generation = tg128 (128 generated tokens, short context). Public rows are medians of community llama.cpp results (sources); our own runs use llama-bench b11658 on a rented GPU.
Speed vs context depth measured
Our own llama-bench runs on GeForce RTX 3060 12GB (flash attention on, 3 repeats): generation and prompt speed with the given number of tokens already in the context.
| Quant | Context depth | Generation tok/s | vs depth 0 | Prompt tok/s |
|---|---|---|---|---|
| Q4_K_M | 0 | 39.7 | 100% | 1277 |
| Q4_K_M | 4k | 37.7 | 95% | 1151 |
| Q4_K_M | 16k | 36.8 | 93% | 951 |
Alternatives
Faster for Gemma 4 12B
- RTX 5070 12GB: Q6_K, 11 GB usable, ~51 tok/s scaled from measured
- RTX 5080 16GB: Q8_0, 15 GB usable, ~62 tok/s measured
- RTX 5070 Ti 16GB: Q8_0, 15 GB usable, ~58 tok/s measured
Other models on RTX 3060 12GB (32GB RAM), similar size
- Qwen3.5 9B: Q8_0, fits fully, ~37 tok/s (measured)
- Ornith 1.5 9B (hardware page): Q8_0, fits fully, ~36 tok/s (scaled from measured)
- Qwen3-VL 8B: Q6_K, fits fully, ~51 tok/s (scaled from measured)
- Gemma 4 E4B (hardware page): Q8_0, fits fully, ~42 tok/s (scaled from measured)
Memory and context limits
Q6_K weights are 9.8 GB; the 16k f16 KV cache adds 0.60 GB. Largest context that fits in 11.0 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | 32k |
| q8_0 | 64k |
| q4_0 | 128k |
Native limit 256k. Full KV cache table for Gemma 4 12B.
Copy-paste commands
The advisor's pick for this hardware: Q6_K, 16k context.
llama-server -hf unsloth/gemma-4-12b-it-GGUF:Q6_K -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:Q6_K
FAQ
How fast is Gemma 4 12B on RTX 3060 12GB (32GB RAM)?
About 39.7 tokens/s for generation (Q4_K_M on llama.cpp CUDA, 128-token generation), 1277 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.
How much memory does Gemma 4 12B need on RTX 3060 12GB (32GB RAM)?
The advisor's pick, Q6_K, is 9.8 GB of weights plus 0.60 GB of KV cache at 16k context (10.4 GB), against 11.0 GB usable. The measured Q4_K_M run needs 7.7 GB.
Is the Gemma 4 12B speed on RTX 3060 12GB (32GB RAM) measured or estimated?
Measured: our own llama-bench run on a rented GeForce RTX 3060 12GB, including runs with 4k to 16k tokens already in context.
What can I use instead of RTX 3060 12GB (32GB RAM) for Gemma 4 12B?
Nothing with less memory in our list fits it fully. Faster: RTX 5070 12GB (~51 tok/s), RTX 5080 16GB (~62 tok/s), RTX 5070 Ti 16GB (~58 tok/s).
What is the maximum context for Gemma 4 12B on RTX 3060 12GB (32GB RAM)?
With an f16 KV cache about 32k tokens, 64k with q8_0 and 128k with q4_0, bounded by the model's own limit.
Related
All models on RTX 3060 12GB (32GB RAM) · Gemma 4 12B on all hardware · VRAM tier · all measured pages · leaderboard
Compare: RTX 3060 12GB (32GB RAM) vs 8GB VRAM class (RTX 4060 8GB, 32GB RAM)