# GLM-5.3 GPU requirements

GLM-5.3 has 753B parameters. To serve it, plan for about 1,808 GB of GPU memory in 16-bit, 904 GB in 8-bit or 452 GB in 4-bit, including about 20% for the KV cache and activations.

It is a mixture of experts: 256 experts, 8 of them active for each token. All of them stay in GPU memory, so plan memory for all 753B parameters.

[Deploy it on Arrakis](/console/#/models?model=zai-org/GLM-5.3).

## What it takes to serve it

| Precision | Memory and the smallest setup |
|---|---|
| 16-bit | 1,808 GB: 8 × [B300](/gpus/b300.html.md), from $36.80 per hour for all 8 |
| 8-bit | 904 GB: 4 × [B300](/gpus/b300.html.md), from $18.40 per hour for all 4 |
| 4-bit | 452 GB: 2 × [B300](/gpus/b300.html.md), from $9.20 per hour for all 2 |

## What it takes to fine-tune it

| Method | Memory and the smallest setup |
|---|---|
| QLoRA | 527 GB: 4 × [H200](/gpus/h200.html.md), from $16.12 per hour for all 4 |
| LoRA | 1,808 GB: 8 × [B300](/gpus/b300.html.md), from $36.80 per hour for all 8 |
| Full fine-tuning | 12,053 GB: more than one 8-GPU machine |

## About the model

| | |
|---|---|
| Parameters | `753B` |
| Experts | `256` |
| Experts for each token | `8` |
| Context window (tokens) | `1,048,576` |
| Published in | `FP8` |
| License | `other` |
| Released | `2026-08-25` |
| Weights | [zai-org/GLM-5.3](https://huggingface.co/zai-org/GLM-5.3) |

## Serve it yourself

```
vllm serve zai-org/GLM-5.3 --tensor-parallel-size 8
```

## Questions

**How much GPU memory does GLM-5.3 need?** To serve it, about 1,808 GB in 16-bit, 904 GB in 8-bit, 452 GB in 4-bit, including about 20% for the KV cache and activations. To fine-tune it, about 527 GB with QLoRA, 1,808 GB with LoRA and 12,053 GB for full fine-tuning with Adam, before activations.

**Can I run GLM-5.3 on one GPU?** No. Even in 4-bit it needs about 452 GB: 2 B300 GPUs.

**What does it cost to run GLM-5.3?** In 16-bit it needs 8 B300 GPUs; in 4-bit, 2 B300 GPUs. Each setup on this page shows the lowest price per GPU-hour on Arrakis right now, and you pay by the hour from a prepaid balance.

**How do I deploy GLM-5.3 on Arrakis?** Open Models in the Arrakis console, paste zai-org/GLM-5.3, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine.


More models: [/models](/models.html.md).