DeepSeek-V4.1-Flash The memory it needs, what you may do with it, and the hardware it suits.
Short answer. DeepSeek-V4.1-Flash needs about 431 GB of memory at 4-bit, 813 GB at 8-bit and 1,529 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 763.2 billion parameters and a 1,048,576-token context window. Licence in short: commercial use allowed. By memory alone, on ANVE’s launch configurations: ANVE One does not fit, ANVE Office does not fit, ANVE Office Pro (GPU) does not fit. These are estimates from its published size, not benchmarks.
01
What it is
DeepSeek-V4.1-Flash is a vision-language model: it reads images (scanned pages, photos, charts and screenshots) as well as text, published by DeepSeek on Hugging Face on 10 September 2026. It has about 763.2 billion parameters and a configured context window of 1,048,576 tokens. It is a mixture-of-experts model: 384 experts, 6 used for each token.
Its card lists the licence as MIT. In short, the licence allows commercial use, modification and redistribution as long as the copyright notice is kept.
Hugging Face counted about 1,255,513 downloads of it in the 30 days before we checked (7 Oct 2026), and 4,213 people have liked it there.
02
How much memory it needs
We size it the way you would size any model: the weights (parameters × bits ÷ 8), the KV cache for 8,192 tokens of context, and roughly 1.5 GB for the runtime itself. Real use varies with settings.
Each token of context adds about 0.08 MB of KV cache at 16-bit, so 32,768 tokens would add about 2.7 GB.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
1,526 GB
0.67 GB
1,529 GB
8-bit (Q8_0)
811 GB
0.67 GB
813 GB
4-bit (Q4_K)
429 GB
0.67 GB
431 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
DeepseekV41ForCausalLM
Model type
deepseek_v41
Layers
40
Hidden size
5,120
Attention heads
64
Key-value heads
1 (grouped-query attention)
Head size
512
Vocabulary
129,280
Experts
384, 6 active per token
Configured context
1,048,576
04
Will it fit on ANVE?
How the verdicts are worked out: on ANVE One, about three quarters of 64 GB of unified memory is available to the model; on ANVE Office and ANVE Office Pro, the model has to fit in the 24 GB on the graphics card, with 64 GB or 128 GB of system memory for everything else. This is arithmetic against the launch specification, not a test. ANVE’s tested default is Qwen 3.8 27B; this model is not yet validated on ANVE.
Configuration (at launch)
Memory for the model
Verdict
ANVE One
about 48 GB of 64 GB unified memory
does not fit
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
does not fit
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
does not fit
05
What an office would use it for
This is frontier-scale: even at 4-bit it needs far more memory than any single office box, so it belongs on a multi-GPU server or a cloud service, not on an appliance.
06
Similar models to compare
Kimi-K2.6 (1026.9B parameters, about 579 GB at 4-bit before context; custom licence: check before commercial use)
MiniMax-VL-01 (456.4B parameters, about 258 GB at 4-bit before context; licence not stated)
MiniMax-M3 (427.0B parameters, about 242 GB at 4-bit before context; custom licence: check before commercial use)
07
Questions people ask
How much memory does DeepSeek-V4.1-Flash need?
About 431 GB at 4-bit, 813 GB at 8-bit and 1,529 GB at full 16-bit precision, including an 8,192-token KV cache and about 1.5 GB of runtime overhead. These are estimates from its parameter count.
Can DeepSeek-V4.1-Flash run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 431 GB, so part of it would sit in system memory, which is much slower.
Is DeepSeek-V4.1-Flash free for commercial use?
Its card lists MIT, which allows commercial use, modification and redistribution as long as the copyright notice is kept. Read the licence itself before relying on this summary; it is not legal advice.
What is DeepSeek-V4.1-Flash’s context window?
Its configuration sets 1,048,576 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
08
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.
