Can you run Gemma 4 12B on RTX 4070?
Gemma 4 12B Q4_K_M on the NVIDIA GeForce RTX 4070: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 4070 runs Gemma 4 12B. 8 GB weights at Q4_K_M vs 10.8 GB usable VRAM; ~37 tok/s est. (ModelFit, 2026).
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Gemma 4 12B on RTX 4070, https://modelfit.io/can-i-run/gemma4-12b-q4-on-rtx-4070/, updated September 2026, CC BY 4.0.
Last updated: September 24, 2026 · Editor: ModelFit Team
What limits this combo
The four constraints the fit engine checks for Gemma 4 12B on the RTX 4070, in order of what usually breaks first.
Where the RTX 4070 sits for Gemma 4 12B
On the RTX 4070, Gemma 4 12B runs with room to spare (~37 tok/s est.). The cheapest card that also runs it comfortably is the RTX 3060 (12 GB, ~30 tok/s est.).
Same engine as the verdict above: 90% of VRAM usable, KV-cache at fp16, bandwidth-derived tok/s. Verdicts of the other cards are computed for Gemma 4 12B Q4_K_M exactly.
Quant explorer
Switch between the quality-gated builds tracked for this exact combo. Q4_K_M stays the default recommendation; heavier Q6/Q8 builds only appear when their exact Ollama tags are registry-verified.
Why no 2-bit builds: Q1/Q2-class quants can look attractive in a memory table, but their quality loss is large enough that ModelFit excludes them from rankings instead of inflating the catalog with junk options. Usable VRAM: 10.8 GB.
Workload verdicts
Gemma 4 12B on the RTX 4070, graded per use case from the model's registry-verified tuning and the engine's speed estimate for this exact combo.
Grades combine tag-verified tuning (a model not built for the workload caps at C) with the tok/s floor each workload needs to feel usable. A combo that partially offloads caps at C; one that does not fit is D everywhere.
Memory math: weights + context vs budget
Weights take 8 GB. Context costs extra KV-cache on top; this is where long-context sessions break on cards that technically fit the weights.
This configuration exceeds the comfortable VRAM budget by about 0.2 GB. Expect offload pressure or a shorter safe context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
See how fast it feels
A deterministic typing simulation for Gemma 4 12B: first token ~0.4s (instant prefill), then ~37 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Gemma 4 12B on RTX 4070: FAQ
Can the NVIDIA GeForce RTX 4070 run Gemma 4 12B?
Yes. Gemma 4 12B (Q4_K_M) loads in about 8 GB and the RTX 4070 offers 10.8 GB of usable VRAM, leaving headroom for context. Expect at roughly 37 tokens/sec (est.).
How much VRAM does Gemma 4 12B need?
About 8 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 3.0 GB extra at 16k tokens. The RTX 4070 budget is 10.8 GB (12 GB x 90%).
What is the best quantization of Gemma 4 12B for the RTX 4070?
Stick with the Q4_K_M build at 8 GB; every heavier quant exceeds the 10.8 GB usable VRAM.
What GPU do I need to run Gemma 4 12B comfortably?
The RTX 4070 already runs Gemma 4 12B comfortably. Larger cards only buy you longer context or a heavier quant.
How much context can Gemma 4 12B use on the RTX 4070?
Up to 8k tokens stay fully in VRAM (9.5 GB total with the KV cache). At 16k tokens the total reaches 11.0 GB and tips over the budget. Quantizing the KV cache to q8_0 roughly halves the KV column and buys back about one context tier.
Is Gemma 4 12B on the RTX 4070 fast enough for coding agents?
Barely on paper: the engine estimates ~37 tok/s against the ~30 tok/s an agentic loop needs to feel responsive, but Gemma 4 12B is not tuned for tool-calling, which caps its agentic grade; a coding-tuned model is the better pick here.