News · 2026-09-10

DeepSeek ships V4.1 Flash as a 510 GB open-weights download

DeepSeek released V4.1 Flash on 10 September 2026, publishing the full weights on Hugging Face under an MIT licence. The model pairs a 552-billion-parameter transformer backbone with a separate 196-billion-parameter memory module, and splits the network into an encoder and a decoder so that reading a long document costs roughly half the computation of writing a reply to it. The published files total 510.3 GB.

Key facts

• 510.3 GB of weights across 48 shards, released under an MIT licence
• Announced 10 September 2026; the repository was created the same day
• Built by DeepSeek, with a technical report published alongside the weights
• Primary source: the Hugging Face model card

Yesterday the story was that DeepSeek appeared to be quietly serving this model without announcing it. Today the company published the weights, the licence and the technical report, and the interesting part turned out not to be the size at all.

The parameter-count argument, settled

Within hours of the release, readers on r/LocalLLaMA were arguing about whether the model is 552 billion parameters or 748 billion. Both figures are correct, and the disagreement is a good way into how the thing is built.

The transformer backbone — the stack of layers that actually does the computing — holds 552 billion parameters. Alongside it sits what DeepSeek calls Engram: "conditional memory (196B parameters, sparsely accessed via token-based lookup)," in the model card's own words. Engram is not a layer the text flows through. It is a lookup table, consulted at two points in the network, that returns stored information associated with the tokens being processed. Add the two and you get 748 billion. DeepSeek reports them separately because they behave differently: one is computation, the other is retrieval.

The distinction matters for anyone running the model. Parameters you compute through cost you time on every token. Parameters you look up cost you memory but almost no arithmetic. It is the difference between a colleague who works out an answer and one who remembers where it is written down.

Reading is cheaper than writing

The larger architectural change is what DeepSeek calls a causal encoder-decoder: "a 20-layer causal encoder followed by a 20-layer decoder." Most large language models today are decoder-only — one uniform stack that treats the text you feed in and the text it produces identically. That is a design choice with a cost, because feeding in a 200-page codebase and generating a two-line fix are very different jobs charged at the same rate.

V4.1 Flash separates them. Roughly 8 billion parameters are active per token while the model is reading its input; roughly 16 billion are active while it is writing output. The decoder's working memory is projected from the encoder's hidden states rather than being built up layer by layer, which is where most of the savings come from.

That is aimed squarely at coding agents, which spend the overwhelming majority of their tokens reading files rather than writing them. It is the same insight that shows up in DeepSeek's own agent harness work: the expensive part of agentic work is context, not output.

The model also compresses its attention cache hard, down to what the card reports as "890 bytes per token" — about a quarter of what the previous V4-Flash generation used at the same sequence length. Context runs to one million tokens, and images can be fed in directly. Like DeepSeek's earlier models it is a mixture of experts: 384 routed experts plus one shared expert per block, with six routed experts firing per token.

What it costs to actually run

The weights occupy 510.3 GB on disk, summed from the repository's own file listing. That is a real number, not an estimate from parameter count, and it is worth stating because the model ships already quantised — the configuration file specifies fp8 weights with fp4 experts, so this is not a full-precision checkpoint waiting to be shrunk.

DeepSeek states no GPU memory requirement anywhere in the repository. What can be said honestly is a floor computed from the shipped files: because the inference code loads the weights at the precision they are stored in, the weights alone occupy roughly 510 GB of resident memory. That is a floor and nothing more — the attention cache, activations and runtime state come on top of it, and at a million tokens of context the cache is not a rounding error. Anyone planning a deployment should treat 510 GB as the beginning of the calculation rather than the answer.

For scale, that is a multi-GPU server, not a workstation. It is also less absurd than it sounds: a 2.8-trillion-parameter model ran on a laptop at one token per second earlier this month, and full Kimi K3 runs on sixteen desktop boxes. The floor for serious open-weight models is high and getting higher.

The honest caveat

Almost everything quantitative here comes from DeepSeek's own technical report and model card. The architecture claims are checkable — the configuration file and the shipped tensors corroborate the parameter counts and the quantisation scheme — but the efficiency comparisons against the previous generation are the company's own measurements, taken on its own hardware, and have not yet been reproduced independently. Cache-size claims in particular are highly sensitive to sequence length and batch composition, which vendors choose. Treat the shape of the result as well established and the magnitude as provisional until someone outside DeepSeek runs it.

Primary source, verified: read the paper →

Ground Truth. (2026, September 10). DeepSeek ships V4.1 Flash as a 510 GB open-weights download. Ground Truth. https://groundtruth.day/news/deepseek-v4-1-flash-ships-as-a-510-gb-open-weights-download.html

@misc{groundtruth:deepseek-v4-1-flash-ships-as-a-510-gb-open-weights-download,
  title  = {DeepSeek ships V4.1 Flash as a 510 GB open-weights download},
  author = {{Ground Truth}},
  year   = {2026},
  month  = {sep},
  url    = {https://groundtruth.day/news/deepseek-v4-1-flash-ships-as-a-510-gb-open-weights-download.html}
}

Topics: deepseek · open-weights · mixture-of-experts · inference · architecture · model-release