Contributed byMarcel SommerGoogle gemma4:31b
Performance benchmark · measured on 03.08.2026 20:47
run-20260804-052133-f1716dPerformance benchmark · Primary metric: Generation-Speed (tok/s) · 1× concurrent
Wie schlägt sich dieser Benchmark mit anderen Modellen?
How does this benchmark compare on other GPUs?
Same model on different hardware · 1× concurrent · Generation (tok/s)
Hardware
GPU: NVIDIA GeForce RTX 3090 Ti · 24 GB VRAM
CPU: AMD Ryzen 5 5600X 6-Core Processor
RAM: 30 GB
Mainboard: ASUSTeK COMPUTER INC. PRIME A520M-K
Setup
Runtime: openclaw_cli
Quantization: -
Model: gemma4:31b
Configuration
| execution_type | local |
gemma4:31b on various hardware
All published performance runs of this model – each bubble a variant: position = prefill (X) × generation (Y), bubble size = number of runs. Closer to the top right = faster. ★ Marked gold = this benchmark.
GPUby graphics card
CPUby processor
MBby mainboard
DRVby driver
Economics of this run
Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (1× concurrent). Methodology →
All values above and the charts below take the configured system utilization into account: at X% the system generates only X% of the time, the rest it idles (25 W). Cost per hour drops (more idle), cost per token rises.
Cost over 2 years – electricity only
Cost over 2 years – incl. acquisition (TCO)
Speed vs. tokens per euro
Euro per 1M tokens
Comparison vs. API – economics per benchmark
| gemma4:31bNVIDIA GeForce RTX 3090 Ti | gemma4:31bNVIDIA GeForce RTX 3090 Ti | gemma4:31b2x AMD Radeon AI PRO R9700 | gemma4:31b2x AMD Radeon AI PRO R9700 | |
|---|---|---|---|---|
| Electricity cost (24 mo.) | – | – | – | – |
| Acquisition cost | – | – | – | – |
| Total cost (TCO) | – | – | – | – |
| Generated tokens (24 mo.) | – | – | – | – |
| Token price via API | – | – | – | – |
| Break-even point (days) | – | – | – | – |
| Result (savings / extra cost) | – | – | – | – |
Comparison with up to 3 next-best runs of this model at the same concurrency (at least one on different hardware). Power = GPU TDP + CPU (idle + 15 %) + board (estimated), acquisition = full system (GPU + CPU + board + RAM + PSU), prices = stored market prices.