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Open-weight model

semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50

by Minhyuk Seo mhseo2/semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50

semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50 is an open-weight model from Minhyuk Seo. It has 8.2B parameters and a 32,768-token context. At 16-bit it needs about 19.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

Parameters8.2B
Context32,768
Weights16.4 GB
License—
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50 (8.2B 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 16.4 GB 19.7 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 8.2 GB 9.8 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 4.1 GB 4.9 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.

semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50 on every accelerator the SAVRN Index prices, at every precision

Model Card

The publisher has not written a card for this model.

Configuration

Architecture
Qwen3ForCausalLM
Context length (tokens)
32,768
Layers
36
Hidden size
4,096
Feed-forward size
12,288
Attention heads
32
Key/value heads
8
Head dimension
128
Vocabulary size
151,936
RoPE base
1,000,000
Model type
qwen3

Identity and Version

Repository
mhseo2/semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50
Publisher
Minhyuk Seo
Task
Not stated by the source
Modality
Other
Library
Not stated by the source
Parameters
8.2B parameters
Languages
Not stated by the source
Revision
af4f74f65b7ba9177f596045c219b2c5a5d873e3
First published
2026-10-17
Last updated
2026-10-17

Files and Weights

15 files, 16.4 GB in total. The weights are 4 files totalling 16.4 GB in safetensors.

Weights4 files · 16.4 GB
Configuration5 files · 35.9 KB
Tokenizer4 files · 15.9 MB
Other1 file · 3.4 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00004.safetensorsWeights4.3 GB 07f2b1bdbf59
model-00002-of-00004.safetensorsWeights4.9 GB 76400596e539
model-00003-of-00004.safetensorsWeights5.0 GB bcc0dd1481f9
model-00004-of-00004.safetensorsWeights2.1 GB 483c5d08c190
added_tokens.jsonConfiguration707 B —
config.jsonConfiguration1.5 KB —
generation_config.jsonConfiguration139 B —
model.safetensors.index.jsonConfiguration32.9 KB —
special_tokens_map.jsonConfiguration613 B —
chat_template.jinjaOther3.4 KB —
.gitattributesRepository1.6 KB —
merges.txtTokenizer1.7 MB —
tokenizer.jsonTokenizer11.4 MB aeb13307a71a
tokenizer_config.jsonTokenizer5.4 KB —
vocab.jsonTokenizer2.8 MB —

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
16.4 GB
Download from Minhyuk Seo

Released by Minhyuk Seo through its official repository on Hugging Face.

Memory Requirements

PrecisionWeights in memory
As published16.4 GB
16-bit16.4 GB
8-bit8.2 GB
4-bit4.1 GB

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

Questions About semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50

How much GPU memory does semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50 need?

About 19.7 GB at 16-bit and 4.9 GB at 4-bit: the weights (8.2B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50 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 semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50's context length?

32,768 tokens, from the maximum position embeddings in its published configuration.