GLM 5.3 Flash
A multimodal GLM model combining sparse experts with hybrid attention.
- Published size
- 320B
- Active parameters
- 18B
- Context window
- Under review
- Architecture
- MoE · vision
- License
- MIT
Deployment considerations
Record the reasoning-effort setting with every test. Different reasoning budgets change generated token counts and end-to-end latency.
The publisher reports 320B total and 18B active parameters. Context limits and full checkpoint memory remain pending verification here.
GPU memory estimates and minimum / recommended configurations are pending. This profile does not contain measured deployment results.
GPU requirements
- Minimum
- —GPU / VRAM · pending
- Recommended
- —GPU / VRAM · pending
Memory estimates and tested configurations will appear here after checkpoint review and deployment testing.
No invented leaderboards.
Latency, throughput, and cost per token will appear here after a reproducible run. Until then, this page helps you understand the model—not predict its performance.
GLM 5.3 Flash deployment FAQ
How much GPU memory does GLM 5.3 Flash need?
The full checkpoint weight footprint is pending review. Minimum and recommended GPU configurations will be added after testing; the model name or active parameter count alone is not a memory requirement.
Has BenchGrid benchmarked GLM 5.3 Flash?
Not yet. This profile contains publisher specifications and calculated weight-memory estimates. We do not currently publish measured latency, throughput, or cost per token for this model.
Where do these specifications come from?
The specifications are based on the official Z.ai model card linked on this page. Memory estimates use the stated total parameter count, including inactive experts for MoE models. Nominal model sizes are labeled with ~.
Explore your compute options.
Check available hardware, quotas, and current pricing with the provider. These links are not verified deployments or performance recommendations.
