13 Aug 2026
DeepSeek-V4-Pro-0813
Keep the production update distinct from the preview
Not verifiedB reported total · text
DeepSeek V4 / V4.1 / MoE
DeepSeek-V4-Pro
Trillion-parameter storage with sparse per-token work
New to model sizes or GPU memory? Start with weights, parameters and quantization →
V4 Pro is the cluster-scale companion to Flash. Its 1.6T store and 49B active path illustrate why sparse compute can make a large model practical without making it a small-model deployment.
1,600Breported total parameters
49Bactive parameters per token
1,048,576configured token positions
Publisher-reported, rounded model count; not an exact tensor census. Multimodal components and packaging can change the artifact size.
Evaluate it for
Large-cluster text evaluations and study of expert-parallel serving.
Choose another path when
Sizing hardware from 49B active parameters or transferring Flash memory results to Pro.
Release context
Why this release matters
The April preview provides a useful large-versus-small comparison within V4. Later Pro and Flash checkpoints should retain their own identities and evaluation records.
Deployment starting points
Which hardware, how many, at what precision?
Start with an exact artifact and an engine that supports it. A configuration below is evidence of a documented path; its device count does not promise a particular throughput or concurrency.
No certified hardware configuration in this edition
Sizing hardware from 49B active parameters or transferring Flash memory results to Pro.
The pinned model card establishes the architecture. It does not, by itself, prove that a particular GPU count runs this checkpoint at your target context. The capacity screen below helps rule out undersized allocations; a successful load and workload test are still required.
864.7 GB of tensor data in the pinned index
This is the index’s reported tensor payload, in decimal GB. It excludes file headers and runtime memory. Mixed precision, conversion and host/device placement determine how much GPU memory the loaded model needs.
Inspect the exact index metadata →Capacity screen: would the weights fit?
This estimates inference memory for a hypothetical uniform precision. It is useful for rejecting an allocation that is too small. It does not establish a working deployment or estimate training memory.
A uniform-precision GPU count would hide this model’s component layout. Publisher-reported, rounded model count; not an exact tensor census. Multimodal components and packaging can change the artifact size. Use the documented artifact and placement path above; no GPU count is inferred here.
Architecture in practice
What the serving engine has to do
Most experts are stored. Only a few execute.
Compute follows selected experts.
Weight memory follows the full expert store.
Mechanism schematic based on the pinned model card and configuration. It explains a design principle; it is not a full implementation graph.
Inspect the full checkpoint specifications
| Exact checkpoint | deepseek-ai/DeepSeek-V4-Pro |
|---|---|
| Text model type | deepseek_v4 |
| Layers | 61 |
| Hidden width | 7,168 |
| Attention / KV heads | 128 / 1 |
| Head dimension | 512 |
| Routed / selected experts | 384 / 6 |
| Configured positions | 1,048,576 |
| Documented extension | Not recorded for this checkpoint |
| Inputs → output | text → text |
| License metadata | mit |
Open weights do not imply unrestricted use. Read the applicable license terms linked from the pinned card.
A useful comparison
What else belongs on the shortlist?
The August update is the same-line comparison; K3 offers another cluster-scale architecture.
27 Jul 2026
Kimi-K3
A cluster model with a new attention design
2,800B reported total · text + image + video
These are editorial comparison candidates. We have not run a matched quality or serving benchmark, so this is not a ranking.
Prepare to explain it
The interview lesson
Explain expert residency, routing traffic and load balance before deriving a throughput estimate.
Attention layouts · Quantization · Courses and worked examples
For a deployment evaluation, record exact weights, precision, engine, device count, interconnect, prompt/output lengths and concurrency. Report task success, errors, TTFT, TPOT and useful throughput together.
Sources and citation
Our assessment is editorial judgment based on the linked architecture and deployment evidence. Model facts are publisher-reported or attributed to runtime maintainers; no independent GPU benchmark was run.
AI Infra Interviews, “DeepSeek-V4-Pro: release, architecture and deployment”, checked 2026-09-13. Preserve this date and the exact checkpoint when citing.