How much memory does Qwen3.5-0.8B need?

About 2.1 GB at 4-bit, 2.5 GB at 8-bit and 3.3 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 Qwen3.5-0.8B run on a 24 GB graphics card?

Yes, at BF16 by this estimate, with room left for context.

What is Qwen3.5-0.8B’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.

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ANVE

Run it on your own hardware · updated 2026-10-16

Qwen3.5-0.8B
How much memory it needs, what its licence allows, and whether it fits an office box.

Short answer. Qwen3.5-0.8B needs about 2.1 GB of memory at 4-bit, 2.5 GB at 8-bit and 3.3 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 873 million parameters and a 262,144-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.

What it is

Qwen3.5-0.8B 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 28 February 2026. It has about 873 million parameters and a configured context window of 262,144 tokens.

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 2,538,229 downloads of it in the 30 days before we checked (7 Oct 2026), and 748 people have liked it there.

It is built on Qwen/Qwen3.5-0.8B-Base, according to its card.

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 0.01 MB of KV cache at 16-bit, so 32,768 tokens would add about 0.40 GB. Only 6 of its 24 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)	1.7 GB	0.10 GB	3.3 GB
8-bit (Q8_0)	0.93 GB	0.10 GB	2.5 GB
4-bit (Q4_K)	0.49 GB	0.10 GB	2.1 GB

Architecture at a glance

Setting	Value in config.json
Architecture class	Qwen3_5ForConditionalGeneration
Model type	qwen3_5
Layers	24
Hidden size	1,024
Attention heads	8
Key-value heads	2 (grouped-query attention)
Head size	256
Vocabulary	248,320
Configured context	262,144
Stored precision	bfloat16

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

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.

Similar models to compare

InternVL2-1B (0.9B parameters, about 2.0 GB at 4-bit before context; commercial use allowed)
InternVL3-1B (0.9B parameters, about 2.0 GB at 4-bit before context; commercial use allowed)
InternVL3_5-1B (1.1B parameters, about 2.1 GB at 4-bit before context; commercial use allowed)

Questions people ask

How much memory does Qwen3.5-0.8B need?: About 2.1 GB at 4-bit, 2.5 GB at 8-bit and 3.3 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 Qwen3.5-0.8B run on a 24 GB graphics card?: Yes, at BF16 by this estimate, with room left for context.
Is Qwen3.5-0.8B 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 Qwen3.5-0.8B’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.

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.

Qwen3.5-0.8BHow much memory it needs, what its licence allows, and whether it fits an office box.