What hardware do I need to run GLM-5.3?

At its most compressed (FP8 (as released)) it needs roughly 750GB of VRAM. VRAM figures are approximate and depend on context length and settings.

Is GLM-5.3 free for commercial use?

Commercial use is permitted, but with conditions. Released under a bespoke GLM-5.3 licence, not MIT. Read the licence before relying on it at scale.

Can I run GLM-5.3 on Apple Silicon?

It can run on Apple Silicon through general runtimes, but it is not specifically optimised for it.

What is GLM-5.3's context window?

GLM-5.3 has a context window of 1,000,000 tokens, about 1000k.

GLM-5.3

Name: GLM-5.3
Author: Z.ai (Zhipu)

Z.ai (Zhipu) · Text generation · MoE 744B (A40B) · 1000k context · Released 14 August 2026

Permitted with conditions Open weights

GLM-5.3 launched as a hosted product on 14 August 2026, with the open weights published on Hugging Face in the final days of the month, after a security review, as zai-org/GLM-5.3 plus a separate BF16 variant. It shares its base model with GLM-5.2, and Z.ai credits the gains to roughly a further month of reinforcement learning rather than a new pretraining run. It is a reasoning model with always-on thinking, exposing three effort levels with no option to disable thinking, a 1-million-token context, and up to 128k output tokens. At this scale it is cluster-class: the realistic route to running it is a production GPU server such as vLLM or SGLang, not a workstation.

Strengths

A frontier-scale open-weight model that can be self-hosted with data kept in-house
Very long 1-million-token context window
Sparse mixture-of-experts, so only about 40B of the 744B parameters compute per token
A bespoke licence that is freely usable commercially for all but hyperscale model hosts

Weaknesses

Firmly server-class, needing a multi-GPU cluster rather than a workstation card
Always-on reasoning, with no option to turn thinking off, adding latency and token use
Headline coding figures are Z.ai's own, and independent verification remains limited
A bespoke licence rather than the MIT terms used for GLM-5.2 and GLM-5.3-Flash

Hardware requirements

Quantisation	Approx. VRAM	Notes
FP8 (as released)	~750GB	The primary released format, roughly 750GB across many files, close to one byte per parameter. This is multi-GPU server territory, and actual needs rise with context and concurrency. Figure is approximate.
BF16	~1500GB	The separately published BF16 variant, roughly 1.5TB. Larger again, for setups that need full precision. Community 4-bit quantisations would cut the FP8 size substantially but remain server-class. Figures are approximate.

What you'd need to run this

Licence

GLM-5.3 License — read the licence

GLM-5.3: common questions

What hardware do I need to run GLM-5.3?: At its most compressed (FP8 (as released)) it needs roughly 750GB of VRAM. VRAM figures are approximate and depend on context length and settings.
Is GLM-5.3 free for commercial use?: Commercial use is permitted, but with conditions. Released under a bespoke GLM-5.3 licence, not MIT. Read the licence before relying on it at scale.
Can I run GLM-5.3 on Apple Silicon?: It can run on Apple Silicon through general runtimes, but it is not specifically optimised for it.
What is GLM-5.3's context window?: GLM-5.3 has a context window of 1,000,000 tokens, about 1000k.

Availability

Recommended for

Organisations with cluster-class infrastructure wanting a frontier-scale open coding model
Server-class self-hosting where data must stay in-house
Long-context agentic coding and knowledge work at scale

Related models

Run it with

Related guides

Glossary

Our coverage

GLM-5.3 open weights land, under a bespoke licence rather than MIT

Catalogue entry last verified 10 September 2026. Specifications change; verify anything you are about to spend money on.