What runs this LLM?
Pick the model you want to serve. We size it from the released checkpoint and list the cheapest configurations you can rent today that hold it β with the on-demand and serverless rate for each.
1 Β· Choose a model
2 Β· What it needs
GLM-5.2 at 16-bit
1,808 GB of VRAM
at short context β measured from the released checkpoint, not estimated from parameter count.
- Model weights
- 1,507 GB
- KV cachefolded into overhead
- β
- Runtime overhead20% of weights
- 301 GB
About this model
- Parameters
- 753B
- Released at
- 16-bit
- Measured weights
- 1,507 GB
3 Β· Where to run it
Bookable configurations
Cheapest first, checked against 98 GPU types with live bookable capacity.
| Configuration | Total VRAM | On-demand | Serverless | GPU details |
|---|---|---|---|---|
| 8Γ AMD Instinct MI300XTight fit | 1,536 GB 272 GB short of the margin | $22.48/hr $2.81/GPU-hr | β | See all prices |
| 8Γ AMD Instinct MI325X | 2,048 GB 240 GB free | $24.45/hr $3.06/GPU-hr | β | See all prices |
| 8Γ NVIDIA B300 | 2,304 GB 496 GB free | $60.00/hr $7.50/GPU-hr | $66.00/hr $8.25/GPU-hr | See all prices |
| 8Γ NVIDIA B300 CC | 2,304 GB 496 GB free | $61.20/hr $7.65/GPU-hr | β | See all prices |
Model weights plus 20% for runtime overhead, from measured checkpoint sizes; a stated context length adds its KV cache on top. Each price is the cheapest bookable rate for that exact instance size, for the whole configuration β the cheaper of the two is highlighted and orders the table. A ΓN figure is total VRAM across N cards and makes no claim about interconnect throughput.
How this is worked out
Weight sizes are measured from the actual checkpoint files on Hugging Face β never derived from parameter count, which gets quantized releases wrong. On top of the weights we assume 20% for activations, CUDA context and allocator slack. Stating a context length adds its KV cache explicitly, computed only for architectures whose cache shape the config states unambiguously.
A configuration that holds the weights and cache but not that full margin is marked as a tight fit rather than hidden β it will load, and may well run at batch size one, but expect trouble under load.
Prices are the cheapest bookable rate for that exact instance size, refreshed with the rest of the catalog.