Qwen3.5-122B-A10B How much memory it needs, what its licence allows, and whether it fits an office box.
Short answer. Qwen3.5-122B-A10B needs about 72 GB of memory at 4-bit, 135 GB at 8-bit and 252 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 125.1 billion parameters and a 262,144-token context window. Licence in short: commercial use allowed. By memory alone, on ANVE’s launch configurations: ANVE One does not fit, 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
Qwen3.5-122B-A10B is a vision-language model: it reads images (scanned pages, photos, charts and screenshots) as well as text, published by Alibaba’s Qwen team on Hugging Face on 24 February 2026. It has about 125.1 billion parameters and a configured context window of 262,144 tokens. It is a mixture-of-experts model: 256 experts, 8 used for each token, so only about 10B parameters are active at a time.
Its card lists the licence as Apache 2.0. In short, the licence allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant.
Hugging Face counted about 573,980 downloads of it in the 30 days before we checked (7 Oct 2026), and 627 people have liked it there.
02
How much memory it needs
The arithmetic: parameters × bits per weight ÷ 8 gives the weights; add the KV cache, which grows with every token of context; then add about 1.5 GB for the runtime. These are estimates for planning, not measurements.
Each token of context adds about 0.02 MB of KV cache at 16-bit, so 32,768 tokens would add about 0.81 GB. Only 12 of its 48 layers use standard attention; the others keep a small fixed state, so the cache is smaller than in a classic transformer.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
250 GB
0.20 GB
252 GB
8-bit (Q8_0)
133 GB
0.20 GB
135 GB
4-bit (Q4_K)
70 GB
0.20 GB
72 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
Qwen3_5MoeForConditionalGeneration
Model type
qwen3_5_moe
Layers
48
Hidden size
3,072
Attention heads
32
Key-value heads
2 (grouped-query attention)
Head size
256
Vocabulary
248,320
Experts
256, 8 active per token
Configured context
262,144
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
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
Llama-4-Scout-17B-16E-Instruct (108.6B parameters, about 63 GB at 4-bit before context; commercial use allowed, with conditions)
GLM-4.5V (107.7B parameters, about 62 GB at 4-bit before context; commercial use allowed)
command-a-plus-05-2026-bf16 (218.8B parameters, about 125 GB at 4-bit before context; commercial use allowed)
07
Questions people ask
How much memory does Qwen3.5-122B-A10B need?
About 72 GB at 4-bit, 135 GB at 8-bit and 252 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 Qwen3.5-122B-A10B run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 72 GB, so part of it would sit in system memory, which is much slower.
Is Qwen3.5-122B-A10B free for commercial use?
Its card lists Apache 2.0, which allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant. Read the licence itself before relying on this summary; it is not legal advice.
What is Qwen3.5-122B-A10B’s context window?
Its configuration sets 262,144 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.
