What GPU do I need to run google/gemma-4-31B-it-assistant?
470M parameters, published in BF16. View on Hugging Face
Set up gemma-4-31B-it-assistantgemma-4-31B-it-assistant is published by google on Hugging Face, with 508,188 downloads and 321 likes to date. It's a Gemma4AssistantForCausalLM model built for any-to-any, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run gemma-4-31B-it-assistant at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-4-31B-it-assistant: common questions
How much VRAM does gemma-4-31B-it-assistant need?
1.0 GB at BF16, 0.5 GB at FP8 (quantized), 0.3 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 0.9 GB of weights plus inference overhead is the whole requirement.
How many copies of gemma-4-31B-it-assistant fit on one RTX 3070?
7, by VRAM alone. That card carries 8.0 GB and one copy needs 1.0 GB at BF16, on a live rate of $0.050/hr for the whole card. Throughput is not modelled here, so 7 copies is not 7 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More google models
- gemma-4-31B-it (31.3B, BF16)
- gemma-4-26B-A4B-it (25.8B, BF16)
- gemma-4-E4B-it (8.0B, BF16)
- gemma-4-E2B-it (5.1B, BF16)
- gemma-4-12B-it (12.0B, BF16)
- gemma-3-1b-it (1000M, BF16)
