Can you run Gemma 4 12B on RTX 4060?
Gemma 4 12B Q4_K_M on the NVIDIA GeForce RTX 4060: verdict, VRAM math and estimated speed.
Yes, but slowly: Gemma 4 12B spills past the RTX 4060. 8 GB weights at Q4_K_M vs 7.2 GB usable VRAM; ~4 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 4060, https://modelfit.io/can-i-run/gemma4-12b-q4-on-rtx-4060/, 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 4060, in order of what usually breaks first.
Where the RTX 4060 sits for Gemma 4 12B
The RTX 4060 is the tightest way to run Gemma 4 12B: it loads with partial offload at ~4 tok/s est.. The cheapest card that runs it without spilling into system RAM is the RTX 3060 (12 GB, ~30 tok/s est.). Context ceiling on this card: none; on the RTX 3060: 8k.
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: 7.2 GB.
Workload verdicts
Gemma 4 12B on the RTX 4060, 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 3.8 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 ~1.5s (fast prefill), then ~4 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Upgrade path
The cheapest tracked card that runs Gemma 4 12B comfortably is the AMD Radeon RX 7900 XT (20 GB VRAM).
See the RX 7900 XT pageGemma 4 12B on RTX 4060: FAQ
Can the NVIDIA GeForce RTX 4060 run Gemma 4 12B?
Barely. Gemma 4 12B (Q4_K_M) needs about 8 GB but the RTX 4060 has 7.2 GB usable VRAM, so part of the model spills to system RAM and speed drops to at roughly 4 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 4060 budget is 7.2 GB (8 GB x 90%).
What is the best quantization of Gemma 4 12B for the RTX 4060?
Stick with the Q4_K_M build at 8 GB; every heavier quant exceeds the 7.2 GB usable VRAM.
What GPU do I need to run Gemma 4 12B comfortably?
The cheapest tracked card that runs Gemma 4 12B (Q4_K_M) fully in VRAM is the AMD Radeon RX 7900 XT (20 GB).
How much context can Gemma 4 12B use on the RTX 4060?
None worth having: the 8 GB of weights alone exceed the 7.2 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is Gemma 4 12B on the RTX 4060 fast enough for coding agents?
No: the engine estimates ~4 tok/s against the ~30 tok/s an agentic loop needs to feel responsive. Chat will feel fine, but multi-step agent runs will drag; a smaller model or a bigger card is the fix.