DeepSeek / MODEL PROFILE

DeepSeek V4.1 Flash

A multimodal model using encoder-decoder attention and KV cache compression.

Data sourcesPublisher specificationsCalculated memory estimatesSource review · Sep 22, 2026
Published size
552B+
Active parameters
See model card
Context window
1M
Architecture
MoE · vision
License
MIT
THE DEPLOYMENT PERSPECTIVE

Deployment considerations

Separate prefill and decode measurements. This architecture activates different parameter counts in each phase and includes a large conditional-memory module.

01

The card lists a 552B backbone plus 196B Engram conditional memory and other modules. Activation is 8B during prefill and 16B during decode, not one fixed number.

02

GPU memory estimates and minimum / recommended configurations are pending. This profile does not contain measured deployment results.

Verify against the upstream model card
DEPLOYMENT CONFIGURATIONPENDING

GPU requirements

Minimum
GPU / VRAM · pending

Memory estimates and tested configurations will appear here after checkpoint review and deployment testing.

THE MEASURED PART COMES NEXT

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.

Read our protocol
A FEW USEFUL ANSWERS

DeepSeek V4.1 Flash deployment FAQ

How much GPU memory does DeepSeek V4.1 Flash 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 DeepSeek V4.1 Flash?

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 DeepSeek 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 ~.

WHEN YOU’RE READY TO EXPERIMENT

Explore your compute options.

Check available hardware, quotas, and current pricing with the provider. These links are not verified deployments or performance recommendations.