Best GPU for Gemma 4 12B Locally
Real-time prices and hardware recommendations updated for August 2026.
Gemma 4 12B is a dense model: all 12B parameters activate on every token, so generation speed is bound by how fast your card can stream the full weights.
To run Gemma 4 12B locally you need roughly 10 GB of VRAM at Q4_K_M quantization with a 64k token context. The best-value card that fits is the Arc B580 (12 GB), which should generate around 13 tokens per second.
Adjust Context Length
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At 64k context, weights + KV cache reaches ~8GB — 12GB cards provide comfortable headroom.
Q4_K_M is the balanced default — fits comfortably on 12-16GB cards with room for a large context window.
Recommended Hardware
Q8_0 is near-lossless for a 12B model. 24GB cards hold the full weights plus a long context.
Other models with the same VRAM requirement
Because Gemma 4 12B's weights fit a 12 GB card, other models of a similar size run on the same GPU. Generation speed varies — mixture-of-experts models are faster, dense models slower — but any of these load in the same VRAM:
Optimizing Setup for Gemma 4 12B
Quantization Recommendations
For daily coding and reasoning tasks, Q4_K_M (4-bit quantization) offers the best balance of quality and memory efficiency — it reduces memory requirements by over 70% with minimal quality loss compared to FP16. Q8 and higher presets preserve more fidelity at the cost of significantly higher VRAM usage, which may force layer offloading and hurt throughput.
Recommended Local Software
We recommend using Ollama as the primary runner for local inference due to its automated GPU model splitting and context cache optimizations. For advanced fine-tuning or quantization splits, llama.cpp with Flash Attention compiled natively provides the best granular control.
Running Gemma 4 12B locally — FAQ
How much VRAM do I need to run Gemma 4 12B?
At a 64k context with KV cache quantization on, Gemma 4 12B needs about 10 GB of VRAM at Q4_K_M — the quantization most people should use. Dropping to Q3_K_M brings that down to roughly 8.2 GB at some quality cost, while Q8_0 needs about 16.9 GB for the best quality this model can give.
What size graphics card does Gemma 4 12B fit on?
Gemma 4 12B needs about 10 GB at Q4_K_M, so a 12 GB card is the smallest common size that holds it entirely in VRAM. Anything smaller has to offload layers to system RAM, which typically costs you most of your generation speed.
Which quantization should I use for Gemma 4 12B?
Use Q4_K_M unless you have VRAM to spare. It needs about 10 GB and loses very little quality against full precision. Q8_0 needs about 16.9 GB for a quality gain most people cannot detect in everyday coding and chat. Spend spare VRAM on a longer context instead.
How does context length affect the VRAM Gemma 4 12B needs?
Model weights are fixed, but the KV cache grows linearly with context. For Gemma 4 12B at a 64k context the cache is about 3.1 GB; doubling to 128k takes it to roughly 6.1 GB. Turning KV cache quantization off doubles those figures again.
Can the same GPU run other models similar to Gemma 4 12B?
Yes. Gemma 4 12B needs about 10 GB at Q4_K_M, and any model whose weights fit the same card runs on it — including Mistral NeMo 12B (12B), SOLAR 10.7B (10.7B), Phi-3 Medium (14B), Phi-4 (14B). The weights fit the same GPU; generation speed varies (mixture-of-experts models are faster, dense models slower).
▸How token speeds are estimated
Two metrics are shown per GPU: Read tok/s (how fast the model ingests your prompt) and Decode tok/s (how fast it streams tokens back). They model fundamentally different bottlenecks.
Read (Prefill)
The prompt is processed in one parallel pass. This is compute-bound: it saturates the GPU's tensor cores.
Decode (Generation)
Each new token requires loading the entire model's active weights from VRAM. This is memory-bandwidth-bound: the GPU stalls waiting for data, not computing.
Weights = (activeParams × bits ÷ 8) × 1.15 overhead. KV cache per step = activeParams × multiplier × contextK.
Architecture utilization factors
Left: decode factor — Right: read factor
Data sources
TFLOPS and memory bandwidth are read from the GPU database. When missing, bandwidth falls back to a hardcoded dictionary.
Limitations
These are analytical estimates, not benchmark results. Use them as a relative comparison, not an absolute performance guarantee.