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Open-weight model

CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only

by brLLMmodels g4me/CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only

CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only is an open-weight model from brLLMmodels. It has 4.4B parameters and a 40,960-token context. At 16-bit it needs about 10.6 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

This model is a trained version of Qwen/Qwen3-4B-Instruct-2507. This is an experimental checkpoint.

Parameters4.4B
Context40,960
Weights16.9 GB
License—
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only (4.4B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 8.8 GB 10.6 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 4.4 GB 5.3 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 2.2 GB 2.6 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.

CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only on every accelerator the SAVRN Index prices, at every precision

Model Card

This model is a trained version of Qwen/Qwen3-4B-Instruct-2507. This is an experimental checkpoint.

Excerpt from the card by brLLMmodels.

Configuration

Architecture
Qwen3ForCausalLM
Context length (tokens)
40,960
Layers
36
Hidden size
2,560
Feed-forward size
9,728
Attention heads
32
Key/value heads
8
Head dimension
128
Vocabulary size
151,936
RoPE base
1e+06
Model type
qwen3

Identity and Version

Repository
g4me/CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only
Publisher
brLLMmodels
Task
Not stated by the source
Modality
Other
Library
Not stated by the source
Parameters
4.4B parameters
Languages
Not stated by the source
Revision
2e91942df8d12b8b2961c646f24145fc69e92a03
First published
2026-10-14
Last updated
2026-10-14

Files and Weights

16 files, 16.9 GB in total. The weights are 4 files totalling 16.9 GB in safetensors.

Weights4 files · 16.9 GB
Configuration5 files · 36.0 KB
Tokenizer4 files · 15.9 MB
Documentation1 file · 532 B
Other1 file · 2.6 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00004.safetensorsWeights5.0 GB d5129c300c26
model-00002-of-00004.safetensorsWeights4.9 GB c3a79806d027
model-00003-of-00004.safetensorsWeights5.0 GB 772427aeb050
model-00004-of-00004.safetensorsWeights1.9 GB dcfdc4cb370d
added_tokens.jsonConfiguration707 B —
config.jsonConfiguration1.5 KB —
generation_config.jsonConfiguration121 B —
model.safetensors.index.jsonConfiguration32.9 KB —
special_tokens_map.jsonConfiguration756 B —
README.mdDocumentation532 B —
chat_template.jinjaOther2.6 KB —
.gitattributesRepository1.6 KB —
merges.txtTokenizer1.7 MB —
tokenizer.jsonTokenizer11.4 MB aeb13307a71a
tokenizer_config.jsonTokenizer9.4 KB —
vocab.jsonTokenizer2.8 MB —

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
16.9 GB
Download from brLLMmodels

Released by brLLMmodels through its official repository on Hugging Face.

Built From

Memory Requirements

PrecisionWeights in memory
As published16.9 GB
16-bit8.8 GB
8-bit4.4 GB
4-bit2.2 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only

How much GPU memory does CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only need?

About 10.6 GB at 16-bit and 2.6 GB at 4-bit: the weights (4.4B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

What is CutIA-Qwen-4B-InstructInit-TF-EduHigh4Only's context length?

40,960 tokens, from the maximum position embeddings in its published configuration.