# GLM-5.3-Flash GPU requirements

GLM-5.3-Flash has 321B parameters. To serve it, plan for about 771 GB of GPU memory in 16-bit, 386 GB in 8-bit or 193 GB in 4-bit, including about 20% for the KV cache and activations.

It is a mixture of experts: 288 experts, 8 of them active for each token. All of them stay in GPU memory, so plan memory for all 321B parameters.

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

## What it takes to serve it

| Precision | Memory and the smallest setup |
|---|---|
| 16-bit | 771 GB: 4 × [B300](/gpus/b300.html.md), from $18.40 per hour for all 4 |
| 8-bit | 386 GB: 2 × [B300](/gpus/b300.html.md), from $9.20 per hour for all 2 |
| 4-bit | 193 GB: one [B300](/gpus/b300.html.md), from $4.60 per GPU-hour |

## What it takes to fine-tune it

| Method | Memory and the smallest setup |
|---|---|
| QLoRA | 225 GB: one [B300](/gpus/b300.html.md), from $4.60 per GPU-hour |
| LoRA | 771 GB: 4 × [B300](/gpus/b300.html.md), from $18.40 per hour for all 4 |
| Full fine-tuning | 5,141 GB: more than one 8-GPU machine |

## About the model

| | |
|---|---|
| Parameters | `321B` |
| Experts | `288` |
| Experts for each token | `8` |
| Context window (tokens) | `1,048,576` |
| Published in | `FP8` |
| License | `mit` |
| Released | `2026-08-25` |
| Weights | [zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash) |

## Serve it yourself

```
vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4
```

## Questions

**How much GPU memory does GLM-5.3-Flash need?** To serve it, about 771 GB in 16-bit, 386 GB in 8-bit, 193 GB in 4-bit, including about 20% for the KV cache and activations. To fine-tune it, about 225 GB with QLoRA, 771 GB with LoRA and 5,141 GB for full fine-tuning with Adam, before activations.

**Can I run GLM-5.3-Flash on one GPU?** Yes, quantized. In 4-bit it needs about 193 GB, which fits on one B300. In 16-bit it needs 4 B300 GPUs.

**What does it cost to run GLM-5.3-Flash?** In 16-bit it needs 4 B300 GPUs; in 4-bit, one B300. 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-Flash on Arrakis?** Open Models in the Arrakis console, paste zai-org/GLM-5.3-Flash, 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).