DeepSeek-V3.2 The memory it needs, what you may do with it, and the hardware it suits.
Short answer. DeepSeek-V3.2 needs about 388 GB of memory at 4-bit, 730 GB at 8-bit and 1,373 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 685.4 billion parameters and a 163,840-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-V3.2 is a language model for chat, drafting and question answering, published by DeepSeek on Hugging Face on 1 December 2025. It has about 685.4 billion parameters and a configured context window of 163,840 tokens. It is a mixture-of-experts model: 256 experts, 8 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 4,240,676 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,507 people have liked it there.
It is built on deepseek-ai/DeepSeek-V3.2-Exp-Base, according to its card.
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 0.07 MB of KV cache at 16-bit, so 32,768 tokens would add about 2.3 GB. This model uses multi-head latent attention, which stores a compressed cache.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
1,371 GB
0.58 GB
1,373 GB
8-bit (Q8_0)
728 GB
0.58 GB
730 GB
4-bit (Q4_K)
386 GB
0.58 GB
388 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
DeepseekV32ForCausalLM
Model type
deepseek_v32
Layers
61
Hidden size
7,168
Attention heads
128
Key-value heads
128
Head size
56
Vocabulary
129,280
Experts
256, 8 active per token
Configured context
163,840
Stored precision
bfloat16
04
Will it fit on ANVE?
These verdicts are arithmetic, not tests, against the specification at launch: ANVE One can give a model about three quarters of its 64 GB of unified memory, while ANVE Office and ANVE Office Pro hold it on a 24 GB graphics card and keep the rest of the stack in 64 GB or 128 GB of system memory. 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
Variants with the same size
DeepSeek-V3-0324: a later dated release of the same model (same parameter count, so the memory figures above apply).
DeepSeek-R1: a reasoning variant that thinks before it answers (same parameter count, so the memory figures above apply).
DeepSeek-V3.1: a variant with the same size and memory needs (same parameter count, so the memory figures above apply).
DeepSeek-R1-0528: a reasoning variant that thinks before it answers (same parameter count, so the memory figures above apply).
DeepSeek-V3.2-Exp: a variant with the same size and memory needs (same parameter count, so the memory figures above apply).
DeepSeek-V3.1-Terminus: a variant with the same size and memory needs (same parameter count, so the memory figures above apply).
07
Similar models to compare
GLM-5.3 (753.3B parameters, about 425 GB at 4-bit before context; custom licence: check before commercial use)
GLM-5.2 (753.3B parameters, about 425 GB at 4-bit before context; commercial use allowed)
Nemotron-3-Ultra-550B-A55B (560.5B parameters, about 317 GB at 4-bit before context; custom licence: check before commercial use)
08
Questions people ask
How much memory does DeepSeek-V3.2 need?
About 388 GB at 4-bit, 730 GB at 8-bit and 1,373 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-V3.2 run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 388 GB, so part of it would sit in system memory, which is much slower.
Is DeepSeek-V3.2 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-V3.2’s context window?
Its configuration sets 163,840 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
09
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.
