DeepSeek-R1-Distill-Qwen-1.5B Memory at each precision, the licence in plain words, and where it fits.
Short answer. DeepSeek-R1-Distill-Qwen-1.5B needs about 2.7 GB of memory at 4-bit, 3.6 GB at 8-bit and 5.3 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 1.8 billion parameters and a 131,072-token context window. Licence in short: commercial use allowed. 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-R1-Distill-Qwen-1.5B is a language model for chat, drafting and question answering, published by DeepSeek on Hugging Face on 20 January 2025. It has about 1.8 billion parameters and a configured context window of 131,072 tokens.
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 1,037,435 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,601 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 0.94 GB.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
3.6 GB
0.23 GB
5.3 GB
8-bit (Q8_0)
1.9 GB
0.23 GB
3.6 GB
4-bit (Q4_K)
1.00 GB
0.23 GB
2.7 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
Qwen2ForCausalLM
Model type
qwen2
Layers
28
Hidden size
1,536
Attention heads
12
Key-value heads
2 (grouped-query attention)
Head size
128
Vocabulary
151,936
Configured context
131,072
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 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
SmolLM2-1.7B-Instruct (1.7B parameters, about 2.5 GB at 4-bit before context; commercial use allowed)
EuroLLM-1.7B-Instruct (1.7B parameters, about 2.4 GB at 4-bit before context; commercial use allowed)
Qwen3-1.7B (2.0B parameters, about 2.6 GB at 4-bit before context; commercial use allowed)
07
Questions people ask
How much memory does DeepSeek-R1-Distill-Qwen-1.5B need?
About 2.7 GB at 4-bit, 3.6 GB at 8-bit and 5.3 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-R1-Distill-Qwen-1.5B run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is DeepSeek-R1-Distill-Qwen-1.5B 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-R1-Distill-Qwen-1.5B’s context window?
Its configuration sets 131,072 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.
