How much memory does Qwen3-30B-A3B need?

About 19 GB at 4-bit, 35 GB at 8-bit and 63 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 Qwen3-30B-A3B run on a 24 GB graphics card?

Yes, at 4-bit by this estimate, with room left for context.

What is Qwen3-30B-A3B’s context window?

Its configuration sets 40,960 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

Qwen3-30B-A3B
Memory at each precision, the licence in plain words, and where it fits.

Short answer. Qwen3-30B-A3B needs about 19 GB of memory at 4-bit, 35 GB at 8-bit and 63 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 30.5 billion parameters and a 40,960-token context window. Licence in short: commercial use allowed. By memory alone, on ANVE’s launch configurations: ANVE One fits at 8-bit, ANVE Office fits at 4-bit, ANVE Office Pro (GPU) fits at 4-bit. These are estimates from its published size, not benchmarks.

What it is

Qwen3-30B-A3B is a language model for chat, drafting and question answering, published by Alibaba’s Qwen team on Hugging Face on 27 April 2025. It has about 30.5 billion parameters and a configured context window of 40,960 tokens. It is a mixture-of-experts model: 128 experts, 8 used for each token, so only about 3B 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 1,457,705 downloads of it in the 30 days before we checked (7 Oct 2026), and 955 people have liked it there.

It is built on Qwen/Qwen3-30B-A3B-Base, according to its card.

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 0.10 MB of KV cache at 16-bit, so 32,768 tokens would add about 3.2 GB.

Precision	Weights	KV cache (8,192 tokens)	Total, estimated
BF16 (full precision)	61 GB	0.81 GB	63 GB
8-bit (Q8_0)	32 GB	0.81 GB	35 GB
4-bit (Q4_K)	17 GB	0.81 GB	19 GB

Architecture at a glance

Setting	Value in config.json
Architecture class	Qwen3MoeForCausalLM
Model type	qwen3_moe
Layers	48
Hidden size	2,048
Attention heads	32
Key-value heads	4 (grouped-query attention)
Head size	128
Vocabulary	151,936
Experts	128, 8 active per token
Configured context	40,960
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 8-bit
ANVE Office	24 GB on the RTX PRO 4000 Blackwell SFF Edition	fits at 4-bit
ANVE Office Pro (GPU)	24 GB on the RTX PRO 4000 Blackwell	fits at 4-bit

What an office would use it for

This is the class ANVE runs by default (27B-class): strong enough for meeting minutes, summaries, drafting and question answering over an office’s own documents, and small enough for one box.

Similar models to compare

GLM-4.7-Flash (31.2B parameters, about 19 GB at 4-bit before context; commercial use allowed)
Nemotron-3-Nano-30B-A3B (31.6B parameters, about 19 GB at 4-bit before context; custom licence: check before commercial use)
granite-4.2-30b (29.3B parameters, about 18 GB at 4-bit before context; commercial use allowed)

Questions people ask

How much memory does Qwen3-30B-A3B need?: About 19 GB at 4-bit, 35 GB at 8-bit and 63 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 Qwen3-30B-A3B run on a 24 GB graphics card?: Yes, at 4-bit by this estimate, with room left for context.
Is Qwen3-30B-A3B 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 Qwen3-30B-A3B’s context window?: Its configuration sets 40,960 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.

Qwen3-30B-A3BMemory at each precision, the licence in plain words, and where it fits.