24 Apr 2026
DeepSeek-V4-Flash
The smaller V4 preview that made cache design central
284B reported total · text
DeepSeek V4 / V4.1 / MoE
DeepSeek-V4-Flash-0731
A post-training update you can compare fairly
New to model sizes or GPU memory? Start with weights, parameters and quantization →
This is the useful July checkpoint when you want to separate training improvements from architecture changes. DeepSeek explicitly says it retains the preview’s architecture and size, which supports using the same 284B/13B scope.
284Breported total parameters
13Bactive parameters per token
1,048,576configured token positions
Publisher confirms the July update retains the preview architecture and size: 284B total, 13B active.
Evaluate it for
Upgrades from V4 Flash preview and reproducible same-architecture comparisons.
Choose another path when
Assuming the September Flash API alias still serves these July weights.
Release context
Why this release matters
The July 31 release updates post-training rather than introducing V4.1’s encoder-decoder and Engram design. Keep the version suffix in evaluation records.
The release note identifies a post-training update with unchanged architecture and size.
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
Assuming the September Flash API alias still serves these July weights.
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.
166.88 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 confirms the July update retains the preview architecture and size: 284B total, 13B active. 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-Flash-0731 |
|---|---|
| Text model type | deepseek_v4 |
| Layers | 43 |
| Hidden width | 4,096 |
| Attention / KV heads | 64 / 1 |
| Head dimension | 512 |
| Routed / selected experts | 256 / 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 preview is the direct baseline; V4.1 is an architectural change rather than another equivalent post-training update.
10 Sept 2026
DeepSeek-V4.1-Flash
The September model that changes the deployment diagram
552B reported total · text + image
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 how fixed architecture helps isolate quality changes while token length and harness behavior can still change service cost.
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-Flash-0731: release, architecture and deployment”, checked 2026-09-13. Preserve this date and the exact checkpoint when citing.