Can 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) run Gemma 4 12B?
Measured results for Gemma 4 12B (11.96B dense) on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM), plus what fits and the commands to run it.
Short answer
Yes. Gemma 4 12B Q8_0 fits fully on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) at 16k context, using 13.3 of 15.0 GB usable, and generates about 20.6 tokens/s in our own run. As the context fills it slows down: 19.6 tok/s with 16k tokens already in context (95% of the empty-context speed), measured.
Measured generation speed by quantization
| Quant | Generation tok/s | Range | Prompt tok/s | Runs | Origin |
|---|---|---|---|---|---|
| Q4 class (median) | 35.4 | 35.4–35.4 | 2186 | 1 | includes our runs |
| Q8 class (median) | 20.6 | 20.6–20.6 | 2216 | 1 | includes our runs |
| Q4_K_M (our run, depth 0) | 35.4 | 3 repeats | 2186 | 3 | own measurement |
| Q8_0 (our run, depth 0) | 20.6 | 3 repeats | 2215 | 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 4060 Ti 16GB (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 | 35.4 | 100% | 2186 |
| Q4_K_M | 4k | 33.5 | 95% | 1983 |
| Q4_K_M | 16k | 32.7 | 92% | 1660 |
| Q8_0 | 0 | 20.6 | 100% | 2215 |
| Q8_0 | 4k | 19.9 | 97% | 2008 |
| Q8_0 | 16k | 19.6 | 95% | 1676 |
Alternatives
Less memory that still fits Gemma 4 12B
Smaller memory usually means a cheaper card or Mac (we do not list prices).
- Mac mini / iMac M4 16GB: Q4_K_M, 11 GB usable, ~11 tok/s scaled from measured
- RTX 5070 12GB: Q6_K, 11 GB usable, ~51 tok/s scaled from measured
- RTX 4070 12GB: Q6_K, 11 GB usable, ~43 tok/s scaled from measured
Faster for Gemma 4 12B
- RTX 3060 12GB (32GB RAM): Q6_K, 11 GB usable, ~29 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 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM), similar size
- Qwen3.5 9B: Q8_0, fits fully, ~30 tok/s (measured)
- Ornith 1.5 9B (hardware page): Q8_0, fits fully, ~30 tok/s (scaled from measured)
- Qwen3-VL 8B: Q8_0, fits fully, ~33 tok/s (measured)
- Gemma 4 E4B (hardware page): Q8_0, fits fully, ~35 tok/s (scaled from measured)
Memory and context limits
Q8_0 weights are 12.7 GB; the 16k f16 KV cache adds 0.60 GB. Largest context that fits in 15.0 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | 64k |
| q8_0 | 128k |
| q4_0 | 256k |
Native limit 256k. Full KV cache table for Gemma 4 12B.
Copy-paste commands
The advisor's pick for this hardware: Q8_0, 16k context.
llama-server -hf unsloth/gemma-4-12b-it-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:Q8_0
FAQ
How fast is Gemma 4 12B on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
About 20.6 tokens/s for generation (Q8_0 on llama.cpp CUDA, 128-token generation), 2215 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.
How much memory does Gemma 4 12B need on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
The advisor's pick, Q8_0, is 12.7 GB of weights plus 0.60 GB of KV cache at 16k context (13.3 GB), against 15.0 GB usable.
Is the Gemma 4 12B speed on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) measured or estimated?
Measured: our own llama-bench run on a rented GeForce RTX 4060 Ti 16GB, including runs with 4k to 16k tokens already in context.
What can I use instead of 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) for Gemma 4 12B?
With less memory, Mac mini / iMac M4 16GB (~11 tok/s), RTX 5070 12GB (~51 tok/s), RTX 4070 12GB (~43 tok/s) also fit it fully. Faster: RTX 3060 12GB (32GB RAM) (~29 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 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
With an f16 KV cache about 64k tokens, 128k with q8_0 and 256k with q4_0, bounded by the model's own limit.
Related
All models on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) · Gemma 4 12B on all hardware · VRAM tier · all measured pages · leaderboard
Compare: RTX 5070 Ti 16GB vs 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) · RTX 5060 Ti 16GB vs 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)