Can you run Gemma 4 26B-A4B on RTX 4060?
Gemma 4 26B-A4B Q4_K_M on the NVIDIA GeForce RTX 4060: verdict, VRAM math and estimated speed.
No, Gemma 4 26B-A4B does not realistically run on the NVIDIA GeForce RTX 4060. 16 GB weights at Q4_K_M vs 7.2 GB usable VRAM; ~9 tok/s est. (ModelFit, 2026).
→ Cheapest tracked card that runs it: AMD Radeon RX 7900 XT (20 GB)
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Gemma 4 26B-A4B on RTX 4060, https://modelfit.io/can-i-run/gemma4-26b-a4b-q4-on-rtx-4060/, updated September 2026, CC BY 4.0.
Last updated: September 18, 2026 · Editor: ModelFit Team
What limits this combo
The four constraints the fit engine checks for Gemma 4 26B-A4B on the RTX 4060, in order of what usually breaks first.
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 26B-A4B 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 16 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 13 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 26B-A4B: first token ~0.9s (instant prefill), then ~9 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 26B-A4B comfortably is the AMD Radeon RX 7900 XT (20 GB VRAM).
See the RX 7900 XT pageGemma 4 26B-A4B on RTX 4060: FAQ
Can the NVIDIA GeForce RTX 4060 run Gemma 4 26B-A4B?
Not realistically. Gemma 4 26B-A4B (Q4_K_M) needs about 16 GB against 7.2 GB usable VRAM on the RTX 4060; even with heavy CPU offload it would run at unusable speed.
How much VRAM does Gemma 4 26B-A4B need?
About 16 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 4.0 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 26B-A4B for the RTX 4060?
Stick with the Q4_K_M build at 16 GB — every heavier quant exceeds the 7.2 GB usable VRAM.
What GPU do I need to run Gemma 4 26B-A4B comfortably?
The cheapest tracked card that runs Gemma 4 26B-A4B (Q4_K_M) fully in VRAM is the AMD Radeon RX 7900 XT (20 GB).
How much context can Gemma 4 26B-A4B use on the RTX 4060?
None worth having — the 16 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 26B-A4B on the RTX 4060 fast enough for coding agents?
No — the model does not realistically fit this card, so agentic loops are out of the question on it.