deepseek-coder-1.3b-instruct Memory at each precision, the licence in plain words, and where it fits.
Short answer. deepseek-coder-1.3b-instruct needs about 3.9 GB of memory at 4-bit, 4.5 GB at 8-bit and 5.8 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 1.3 billion parameters and a 16,384-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 BF16, ANVE Office Pro (GPU) fits at BF16. These are estimates from its published size, not benchmarks.
01
What it is
deepseek-coder-1.3b-instruct is a language model for chat, drafting and question answering, published by DeepSeek on Hugging Face on 29 October 2023. It has about 1.3 billion parameters and a configured context window of 16,384 tokens.
Its card lists the licence as a custom licence (deepseek). In short, the licence sets its own terms; read it before any commercial use.
Hugging Face counted about 40,841 downloads of it in the 30 days before we checked (7 Oct 2026), and 185 people have liked it there.
02
How much memory it needs
How these figures are worked out: weights = parameters × bits per weight ÷ 8. The KV cache is added for 8,192 tokens of context, plus about 1.5 GB of runtime overhead. Treat the totals as planning estimates.
Each token of context adds about 0.20 MB of KV cache at 16-bit, so 32,768 tokens would add about 6.4 GB.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
2.7 GB
1.6 GB
5.8 GB
8-bit (Q8_0)
1.4 GB
1.6 GB
4.5 GB
4-bit (Q4_K)
0.76 GB
1.6 GB
3.9 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
LlamaForCausalLM
Model type
llama
Layers
24
Hidden size
2,048
Attention heads
16
Key-value heads
16
Head size
128
Vocabulary
32,256
Configured context
16,384
Stored precision
bfloat16
04
Will it fit on ANVE?
Arithmetic against the launch specification, not a test: about three quarters of ANVE One’s 64 GB of unified memory for the model (as AMD’s Variable Graphics Memory allows), and the 24 GB on the graphics card in ANVE Office and ANVE Office Pro, which keep the rest of the stack in 64 GB and 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 BF16
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
fits at BF16
05
What an office would use it for
Models this small are best for routing, classification, autocomplete and simple extraction. They are not reliable for minutes or legal drafting: ANVE never uses anything smaller than a 4B-class model for minutes.
06
Similar models to compare
granite-3.0-1b-a400m-instruct (1.3B parameters, about 2.3 GB at 4-bit before context; commercial use allowed)
Llama-3.2-1B-Instruct (1.2B parameters, about 2.2 GB at 4-bit before context; commercial use allowed, with conditions)
OLMo-2-0425-1B-Instruct (1.5B parameters, about 2.3 GB at 4-bit before context; commercial use allowed)
07
Questions people ask
How much memory does deepseek-coder-1.3b-instruct need?
About 3.9 GB at 4-bit, 4.5 GB at 8-bit and 5.8 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-1.3b-instruct run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is deepseek-coder-1.3b-instruct free for commercial use?
Its card lists a custom licence (deepseek), 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-1.3b-instruct’s context window?
Its configuration sets 16,384 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.
