High-memory GPUs on OneInfer
A single-GPU instance below is rarely enough for a model this size — expect a multi-GPU node. These are current listings by VRAM, for scoping a self-host deployment once weights are available.
VRAM by precision
| Precision | Approximate VRAM | Notes |
|---|---|---|
| FP16 / full precision | ~1.5 TB | Multi-GPU datacenter deployment only. |
| FP8 | ~750 GB | Halves memory at typical accuracy cost for this model class. |
| 2-bit quantized | ~245 GB | The realistic self-host floor; expect measurable quality loss. |
Weights are not released yet
Self-hosting GLM-5.3 requires open weights, which Z.ai has not shipped as of this page’s last verification. Treat every figure above as an architecture-driven estimate until a real checkpoint and license are confirmed.
Ready to test the workflow?
Create account & add creditsArchitecture
GLM-5.3 uses a mixture-of-experts design: roughly 744B total parameters with about 40B active per token, which is why its serving cost sits well below a dense model of comparable total size.
Frequently asked questions
What GPU do I need to run GLM-5.3?
Roughly 1.5TB of VRAM at full precision, or about 245GB at 2-bit quantization — realistically a multi-GPU datacenter node once open weights ship.
Is GLM-5.3 open source?
Open weights were promised for late August 2026 with an MIT license expected but unconfirmed as of this page’s last verification. At launch, GLM-5.3 was API-available only.
Where is the live GLM-5.3 model page?
The canonical model page with current OneInfer pricing, capabilities, and availability is /models/zai-org/GLM-5.3. This page is a focused facet of that entity, not a replacement for it.
How should I treat benchmark or price claims?
Check each claim’s provenance label and observed date. Vendor-reported and independently verified numbers are shown as separate evidence classes on this hub.
Put GLM-5.3 to work
Fund a controlled evaluation, start with a prepared prompt, and measure quality and cost on your own workload.