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

Apertus-8B-Instruct-2509
How much memory it needs, what its licence allows, and whether it fits an office box.

Short answer. Apertus-8B-Instruct-2509 needs about 7.1 GB of memory at 4-bit, 11 GB at 8-bit and 19 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 8.1 billion parameters and a 65,536-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

Apertus-8B-Instruct-2509 is a language model for chat, drafting and question answering, published by the Swiss AI Initiative (EPFL and ETH Zurich) on Hugging Face on 13 August 2025. It has about 8.1 billion parameters and a configured context window of 65,536 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 452,881 downloads of it in the 30 days before we checked (7 Oct 2026), and 495 people have liked it there.

It is built on swiss-ai/Apertus-8B-2509, according to its card.

02

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 0.13 MB of KV cache at 16-bit, so 32,768 tokens would add about 4.3 GB.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)16 GB1.1 GB19 GB
8-bit (Q8_0)8.6 GB1.1 GB11 GB
4-bit (Q4_K)4.5 GB1.1 GB7.1 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classApertusForCausalLM
Model typeapertus
Layers32
Hidden size4,096
Attention heads32
Key-value heads8 (grouped-query attention)
Head size128
Vocabulary131,072
Configured context65,536
Stored precisionbfloat16
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 modelVerdict
ANVE Oneabout 48 GB of 64 GB unified memoryfits at BF16
ANVE Office24 GB on the RTX PRO 4000 Blackwell SFF Editionfits at BF16
ANVE Office Pro (GPU)24 GB on the RTX PRO 4000 Blackwellfits at BF16
05

What an office would use it for

This size suits quick drafting, classification, extraction and chat over short documents on modest hardware. ANVE pairs a fast 4B-class model with its main model for quick jobs.

06

Variants with the same size

  • Apertus-8B-2509: a later dated release of the same model (same parameter count, so the memory figures above apply).
07

Similar models to compare

  • DeepSeek-R1-Distill-Llama-8B (8.0B parameters, about 6.0 GB at 4-bit before context; commercial use allowed)
  • Llama-3.1-8B-Instruct (8.0B parameters, about 6.0 GB at 4-bit before context; commercial use allowed, with conditions)
  • Meta-Llama-3-8B-Instruct (8.0B parameters, about 6.0 GB at 4-bit before context; commercial use allowed, with conditions)
08

Questions people ask

How much memory does Apertus-8B-Instruct-2509 need?
About 7.1 GB at 4-bit, 11 GB at 8-bit and 19 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 Apertus-8B-Instruct-2509 run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is Apertus-8B-Instruct-2509 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 Apertus-8B-Instruct-2509’s context window?
Its configuration sets 65,536 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
09

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.