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Open-weight model

GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid

by Jarrel Seah jarrelscy/GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid

GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid is an open-weight model from Jarrel Seah. It has 186.8B parameters and a 1,048,576-token context. At 16-bit it needs about 448.4 GB of GPU memory, which fits on 2x MI325X from $4.00 an hour; at 4-bit, 112.1 GB on 1x MI300X from $1.85, at the lowest prices in the SAVRN Index.

Per-expert ARVQ v3 cold experts, NVFP4 hot experts, ARVQ-scored REAP allocation. 75/75 MoE layers replaced by the full-corpus sequential PV campaign. Other layers retain their previously published weights; see pvprogress.json.

Parameters186.8B
Context1,048,576
Weights312.2 GB
License—
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid (186.8B 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 373.7 GB 448.4 GB 2x MI325X (256 GB)
Vultr		 $4.00 2x MI355X $5.18 · 3x MI300X $5.55
8-bit 186.8 GB 224.2 GB 1x MI325X (256 GB)
Vultr		 $2.00 1x MI355X $2.59 · 2x MI300X $3.70
4-bit 93.4 GB 112.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x MI325X $2.00 · 1x MI355X $2.59

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 1, 2026.

GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid on every accelerator the SAVRN Index prices, at every precision

Model Card

Per-expert ARVQ v3 cold experts, NVFP4 hot experts, ARVQ-scored REAP allocation. 75/75 MoE layers replaced by the full-corpus sequential PV campaign. Other layers retain their previously published weights; see pvprogress.json. Each layer's two tensor files and reports are replaced together in one commit. Training draws sequentially from 18,001,846 text tokens at context 1024. Fixed validation and development-audit sets each contain 16,384 tokens. Adam trains FP4-constrained per-expert books and FP8-constrained per-block scales. The effective batch is 262,144 tokens, accumulated in four 65,536-token passes. Layers 4–26 use 69 updates: book/scale LR.048/.032 through update 45, then.012/.008.…

Excerpt from the card by Jarrel Seah.

Configuration

Architecture
Glm5vForConditionalGeneration
Context length (tokens)
1,048,576
Layers
78
Hidden size
6,144
Feed-forward size
12,288
Attention heads
64
Key/value heads
64
Head dimension
64
Vocabulary size
154,880
Routed experts
256
Experts active per token
8
Model type
glm5v
Quantization
nvfp4_arvq_hybrid

Identity and Version

Repository
jarrelscy/GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid
Publisher
Jarrel Seah
Task
Not stated by the source
Modality
Other
Library
vllm
Parameters
186.8B parameters
Languages
Not stated by the source
Revision
be8cd37751f667203448e5595df01ca0ef4f6000
First published
2026-09-20
Last updated
2026-09-21

Files and Weights

446 files, 312.2 GB in total. The weights are 274 files totalling 312.2 GB in safetensors.

Weights274 files · 312.2 GB
Configuration166 files · 3.4 MB
Tokenizer2 files · 20.2 MB
Documentation2 files · 19.5 KB
Other1 file · 10.7 KB
Repository1 file · 1.6 KB
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License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
312.2 GB
Download from Jarrel Seah

Released by Jarrel Seah through its official repository on Hugging Face.

Memory Requirements

PrecisionWeights in memory
As published312.2 GB
16-bit373.7 GB
8-bit186.8 GB
4-bit93.4 GB

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

Questions About GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid

How much GPU memory does GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid need?

About 448.4 GB at 16-bit and 112.1 GB at 4-bit: the weights (186.8B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid on?

At 16-bit, 2x MI325X from $4.00 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

What is GLM-5.3-Vision-NVFP4-ARVQ-v2-hybrid's context length?

1,048,576 tokens, from the maximum position embeddings in its published configuration.