Can the Radeon RX 7900 XTX run Gemma 3 4B IT?
Fits comfortably at Q4_K_M.
Gemma 3 4B IT is 4.3B parameters. At Q4_K_M the weights are about 2.4 GiB; with the 1.5 GiB runtime reserve that is 3.9 GiB against the card's 24GB, leaving about 20.1 GiB for context.
All three quants
24GB card| Quant | Weights | With reserve | Verdict | Headroom |
|---|---|---|---|---|
| Q4_K_Mthe everyday quant | 2.4 GiB | 3.9 GiB | Fits comfortably | 20.1 GiB |
| Q8_0near-lossless | 4.2 GiB | 5.7 GiB | Fits comfortably | 18.3 GiB |
| FP16full weights | 8.0 GiB | 9.5 GiB | Fits comfortably | 14.5 GiB |
Verdict rules: fits comfortably when weights plus reserve sit within 85% of VRAM; tight when they fit with little left for context; CPU offload when they exceed VRAM by up to 2x (runs, slowly, with layers in system RAM); no beyond that. The KV cache per token depends on the model's architecture and is not modelled here.
The numbers for this pairing
At Q4_K_M the weights take about 2.4 GiB; with the 1.5 GiB we set aside for the runtime that is 3.9 of the card's 24 GB, leaving about 20.1 GB for the context cache.
The highest-quality version that fits entirely in memory is FP16 (full quality).
The cheapest card on our list that runs it comfortably at Q4 is the Arc B570 at about $260, against about $850 for this card.
At about $850, this card costs roughly $35 per GB of VRAM.
Running it at its full 355 W board power for 4 hours a day would use about 43 kWh a month, about $7.84 at the US average residential rate of 18.4 cents per kWh (2026-05); real use sits below that ceiling.
Prices are the lowest current listings our tracker saw on Oct 7, 2026, since averages include premium variants; launch prices are marked where we have no listing.
Good match. Get the card.
The Radeon RX 7900 XTX clears Gemma 3 4B IT at Q4_K_M and at Q8_0. Live listings below; this page was rebuilt 2026-10-07.
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About the model
Gemma 3 4B IT by Google: 131,072-token native context, text + vision, released 2025 under the Gemma Terms of Use. Model card → · Hosted pricing on PicksByModel →