Qwen3-Reranker-8B The memory it needs, what you may do with it, and the hardware it suits.
Short answer. Qwen3-Reranker-8B needs about 6.1 GB of memory at 4-bit, 10 GB at 8-bit and 18 GB at full 16-bit precision, including runtime overhead. It has about 8.2 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 BF16, ANVE Office fits at BF16, ANVE Office Pro (GPU) fits at BF16. These are estimates from its published size, not benchmarks.
01
What it is
Qwen3-Reranker-8B is a reranker that re-orders search results so the most relevant passages come first, published by Alibaba’s Qwen team on Hugging Face on 29 May 2025. It has about 8.2 billion parameters and a configured context window of 40,960 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 406,418 downloads of it in the 30 days before we checked (7 Oct 2026), and 275 people have liked it there.
It is built on Qwen/Qwen3-8B-Base, according to its card.
02
How much memory it needs
The arithmetic: parameters × bits per weight ÷ 8 gives the weights, plus about 1.5 GB for the runtime. These are planning estimates, not measurements.
Inputs are short, so working memory is small next to the weights.
Precision
Weights
Total, estimated
BF16 (full precision)
16 GB
18 GB
8-bit (Q8_0)
8.7 GB
10 GB
4-bit (Q4_K)
4.6 GB
6.1 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
Qwen3ForCausalLM
Model type
qwen3
Layers
36
Hidden size
4,096
Attention heads
32
Key-value heads
8 (grouped-query attention)
Head size
128
Vocabulary
151,669
Configured context
40,960
Stored precision
bfloat16
04
Will it fit on ANVE?
How the verdicts are worked out: on ANVE One, about three quarters of 64 GB of unified memory is available to the model; on ANVE Office and ANVE Office Pro, the model has to fit in the 24 GB on the graphics card, with 64 GB or 128 GB of system memory for everything else. This is arithmetic against the launch specification, not a test. 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
05
What an office would use it for
A reranker sits after search: it reads the top results and the question together and puts the best passages first, which improves the answers a document assistant gives.
06
Similar models to compare
llama-nemotron-rerank-1b-v2 (1.2B parameters, about 2.2 GB at 4-bit before context; custom licence: check before commercial use)
jina-reranker-v3 (0.6B parameters, about 1.8 GB at 4-bit before context; non-commercial only)
bge-reranker-large (0.6B parameters, about 1.8 GB at 4-bit before context; commercial use allowed)
07
Questions people ask
How much memory does Qwen3-Reranker-8B need?
About 6.1 GB at 4-bit, 10 GB at 8-bit and 18 GB at full 16-bit precision, including about 1.5 GB of runtime overhead. These are estimates from its parameter count.
Can Qwen3-Reranker-8B run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is Qwen3-Reranker-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-Reranker-8B’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.
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.
