Qwen3.8-Flash-Next Memory at each precision, the licence in plain words, and where it fits.
Short answer. Qwen3.8-Flash-Next needs about 103 GB of memory at 4-bit, 193 GB at 8-bit and 362 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 180 billion parameters and a 262,144-token context window. Licence in short: custom licence: check before commercial use. By memory alone, on ANVE’s launch configurations: ANVE One does not fit, ANVE Office does not fit, 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
Qwen3.8-Flash-Next 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 August 2026. It has about 180 billion parameters and a configured context window of 262,144 tokens. It is a mixture-of-experts model: 512 experts, 10 used for each token.
Its card lists the licence as a custom licence (qwen-community-1.0). In short, the licence sets its own terms; read it before any commercial use.
Hugging Face counted about 1,609,433 downloads of it in the 30 days before we checked (7 Oct 2026), and 6,003 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 0.02 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)
360 GB
0.20 GB
362 GB
8-bit (Q8_0)
191 GB
0.20 GB
193 GB
4-bit (Q4_K)
101 GB
0.20 GB
103 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
Qwen4ExpForConditionalGeneration
Model type
qwen4_exp
Layers
48
Hidden size
2,560
Attention heads
24
Key-value heads
2 (grouped-query attention)
Head size
256
Vocabulary
248,320
Experts
512, 10 active per token
Configured context
262,144
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
does not fit
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
does not fit
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
This is frontier-scale: even at 4-bit it needs far more memory than any single office box, so it belongs on a multi-GPU server or a cloud service, not on an appliance.
06
Similar models to compare
command-a-plus-05-2026-bf16 (218.8B parameters, about 125 GB at 4-bit before context; commercial use allowed)
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)
07
Questions people ask
How much memory does Qwen3.8-Flash-Next need?
About 103 GB at 4-bit, 193 GB at 8-bit and 362 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 Qwen3.8-Flash-Next run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 103 GB, so part of it would sit in system memory, which is much slower.
Is Qwen3.8-Flash-Next free for commercial use?
Its card lists a custom licence (qwen-community-1.0), 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 Qwen3.8-Flash-Next’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.
