Gemma 4 26B-A4B
Gemma's sparse vision-language model, a useful counterpart to the dense 31B.
- Published size
- ~26B
- Active parameters
- 3.8B
- Context window
- 256K
- Architecture
- MoE · vision
- License
- Apache 2.0
Deployment considerations
Compare this MoE with Gemma 4 31B at the same precision and workload to separate weight memory from active compute.
The card lists 25.2B backbone parameters, 3.8B active parameters, and an approximately 550M-parameter vision encoder.
GPU memory estimates and minimum / recommended configurations are pending. This profile does not contain measured deployment results.
GPU requirements
- Minimum
- —GPU / VRAM · pending
- Recommended
- —GPU / VRAM · pending
Memory estimates and tested configurations will appear here after checkpoint review and deployment testing.
No invented leaderboards.
Latency, throughput, and cost per token will appear here after a reproducible run. Until then, this page helps you understand the model—not predict its performance.
Gemma 4 26B-A4B deployment FAQ
How much GPU memory does Gemma 4 26B-A4B need?
The full checkpoint weight footprint is pending review. Minimum and recommended GPU configurations will be added after testing; the model name or active parameter count alone is not a memory requirement.
Has BenchGrid benchmarked Gemma 4 26B-A4B?
Not yet. This profile contains publisher specifications and calculated weight-memory estimates. We do not currently publish measured latency, throughput, or cost per token for this model.
Where do these specifications come from?
The specifications are based on the official Google model card linked on this page. Memory estimates use the stated total parameter count, including inactive experts for MoE models. Nominal model sizes are labeled with ~.
Explore your compute options.
Check available hardware, quotas, and current pricing with the provider. These links are not verified deployments or performance recommendations.
