Hunyuan-A13B-Instruct Memory at each precision, the licence in plain words, and where it fits.
Short answer. Hunyuan-A13B-Instruct needs about 48 GB of memory at 4-bit, 88 GB at 8-bit and 163 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 80.4 billion parameters and a 32,768-token context window. Licence in short: custom licence: check before commercial use. By memory alone, on ANVE’s launch configurations: ANVE One fits at 4-bit, ANVE Office only with partial offload to system memory, which is much slower, ANVE Office Pro (GPU) only with partial offload to system memory, which is much slower. These are estimates from its published size, not benchmarks.
01
What it is
Hunyuan-A13B-Instruct is a language model for chat, drafting and question answering, published by Tencent on Hugging Face on 25 June 2025. It has about 80.4 billion parameters and a configured context window of 32,768 tokens. It is a mixture-of-experts model with about 13B parameters active per token.
Its card lists the licence as a custom licence (tencent-hunyuan-a13b). In short, the licence sets its own terms; read it before any commercial use.
Hugging Face counted about 71,505 downloads of it in the 30 days before we checked (7 Oct 2026), and 795 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 0.13 MB of KV cache at 16-bit, so 32,768 tokens would add about 4.3 GB.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
161 GB
1.1 GB
163 GB
8-bit (Q8_0)
85 GB
1.1 GB
88 GB
4-bit (Q4_K)
45 GB
1.1 GB
48 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
HunYuanMoEV1ForCausalLM
Model type
hunyuan_v1_moe
Layers
32
Hidden size
4,096
Attention heads
32
Key-value heads
8 (grouped-query attention)
Head size
128
Vocabulary
128,167
Experts
64
Configured context
32,768
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 4-bit
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
only with partial offload to system memory, which is much slower
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
only with partial offload to system memory, which is much slower
05
What an office would use it for
Models this large are stronger on long, hard drafting and reasoning, but need most of a 64 to 128 GB machine even at 4-bit, which leaves less room for long documents and several users.
06
Similar models to compare
Qwen3-Coder-Next (79.7B parameters, about 46 GB at 4-bit before context; commercial use allowed)
Apertus-70B-2509 (70.6B parameters, about 41 GB at 4-bit before context; commercial use allowed)
DeepSeek-R1-Distill-Llama-70B (70.6B parameters, about 41 GB at 4-bit before context; commercial use allowed)
07
Questions people ask
How much memory does Hunyuan-A13B-Instruct need?
About 48 GB at 4-bit, 88 GB at 8-bit and 163 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 Hunyuan-A13B-Instruct run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 48 GB, so part of it would sit in system memory, which is much slower.
Is Hunyuan-A13B-Instruct free for commercial use?
Its card lists a custom licence (tencent-hunyuan-a13b), 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 Hunyuan-A13B-Instruct’s context window?
Its configuration sets 32,768 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.
