DeepSeek-Coder-V2-Lite-Instruct How much memory it needs, what its licence allows, and whether it fits an office box.
Short answer. DeepSeek-Coder-V2-Lite-Instruct needs about 11 GB of memory at 4-bit, 18 GB at 8-bit and 33 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 15.7 billion parameters and a 163,840-token context window. Licence in short: custom licence: check before commercial use. By memory alone, on ANVE’s launch configurations: ANVE One fits at BF16, ANVE Office fits at 8-bit, ANVE Office Pro (GPU) fits at 8-bit. These are estimates from its published size, not benchmarks.
01
What it is
DeepSeek-Coder-V2-Lite-Instruct is a language model for chat, drafting and question answering, published by DeepSeek on Hugging Face on 14 June 2024. It has about 15.7 billion parameters and a configured context window of 163,840 tokens. It is a mixture-of-experts model: 64 experts, 6 used for each token.
Its card lists the licence as a custom licence (deepseek-license). In short, the licence sets its own terms; read it before any commercial use.
Hugging Face counted about 914,538 downloads of it in the 30 days before we checked (7 Oct 2026), and 668 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 0.03 MB of KV cache at 16-bit, so 32,768 tokens would add about 1.0 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)
31 GB
0.25 GB
33 GB
8-bit (Q8_0)
17 GB
0.25 GB
18 GB
4-bit (Q4_K)
8.8 GB
0.25 GB
11 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
DeepseekV2ForCausalLM
Model type
deepseek_v2
Layers
27
Hidden size
2,048
Attention heads
16
Key-value heads
16
Head size
128
Vocabulary
102,400
Experts
64, 6 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
fits at BF16
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
fits at 8-bit
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
fits at 8-bit
05
What an office would use it for
This is the class ANVE runs by default (27B-class): strong enough for meeting minutes, summaries, drafting and question answering over an office’s own documents, and small enough for one box.
06
Variants with the same size
DeepSeek-V2-Lite-Chat: the instruction-tuned version for chat (same parameter count, so the memory figures above apply).
07
Similar models to compare
Moonlight-16B-A3B-Instruct (16.0B parameters, about 10 GB at 4-bit before context; commercial use allowed)
Qwen2.5-Coder-14B-Instruct (14.8B parameters, about 9.8 GB at 4-bit before context; commercial use allowed)
Qwen3-14B (14.8B parameters, about 9.8 GB at 4-bit before context; commercial use allowed)
08
Questions people ask
How much memory does DeepSeek-Coder-V2-Lite-Instruct need?
About 11 GB at 4-bit, 18 GB at 8-bit and 33 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-Coder-V2-Lite-Instruct run on a 24 GB graphics card?
Yes, at 8-bit by this estimate, with room left for context.
Is DeepSeek-Coder-V2-Lite-Instruct free for commercial use?
Its card lists a custom licence (deepseek-license), which sets its own terms; read it before any commercial use. Read the licence itself before relying on this summary; it is not legal advice.
What is DeepSeek-Coder-V2-Lite-Instruct’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.
