# Minimum GPU Memory Requirements for GLM-5 Inference: Complete Hardware Guide

> Discover GLM-5 inference GPU memory needs. Learn how BF16 needs 40GB, while FP8 quantization drops requirements to 24GB, enabling consumer hardware use.

- Repository: [Z.ai/GLM-5](https://github.com/zai-org/GLM-5)
- Tags: hardware-guide
- Published: 2026-06-19

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**GLM-5 inference requires at least 40GB of GPU memory for the BF16 precision variant, while the FP8 quantized version reduces this requirement to 24GB, making it compatible with consumer-grade hardware like the NVIDIA RTX 3090.**

Determining the minimum GPU memory requirements for GLM-5 inference is critical for deploying the zai-org/GLM-5 model series efficiently. The GLM-S variant—a 744-billion-parameter model with approximately 40 billion active parameters—offers two distinct precision modes that directly impact hardware requirements. According to the repository's documentation in [`README.md`](https://github.com/zai-org/GLM-5/blob/main/README.md) (lines 63-68), selecting the appropriate checkpoint determines whether you need high-end server GPUs or can run inference on consumer hardware.

## Model Specifications and Memory Footprint

### Architecture and Parameter Count

The GLM-S model architecture is detailed in the [`README.md`](https://github.com/zai-org/GLM-5/blob/main/README.md) file (lines 63-68), which lists the model sizes and available precisions. GLM-S features a total parameter count of 744 billion, with an **active-parameter footprint of approximately 40 billion** parameters during inference. This sparse activation pattern allows the model to maintain high performance while optimizing memory usage compared to dense models of similar scale.

### Precision Modes and Memory Consumption

The repository provides two primary checkpoint variants that affect GPU memory requirements:

- **BF16 (BFloat16)**: The default precision requiring approximately **40GB of GPU memory**
- **FP8 (8-bit floating point)**: The quantized variant reducing memory requirements to roughly **24GB**

## Hardware Requirements by Precision

### BF16 Deployment (40GB Minimum)

According to the [`README.md`](https://github.com/zai-org/GLM-5/blob/main/README.md) download table, the standard GLM-S checkpoint uses BF16 precision. This variant requires **at least 40GB of GPU memory** for single-GPU inference. Suitable hardware includes NVIDIA A100 (40GB or 80GB), A6000, or multiple consumer GPUs configured with tensor parallelism.

### FP8 Quantized Deployment (24GB Minimum)

The `GLM-S-FP8` checkpoint offers a memory-efficient alternative. By leveraging FP8 quantization (supported in recent PyTorch versions), the model's memory footprint decreases by approximately 50%. This reduction enables deployment on **24GB GPUs** such as the NVIDIA RTX 3090 or RTX 4090, significantly lowering the barrier to entry for local inference.

## Implementation Examples

The following code examples demonstrate loading each variant using the `transformers` library. These implementations reference the exact model identifiers specified in the zai-org/GLM-5 repository.

Standard BF16 loading (requires ≥40GB GPU):

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "zai-org/GLM-5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="bfloat16",   # BF16 precision

    device_map="auto"        # Automatically places the model on the GPU

)

```

FP8 quantized loading (requires ≥24GB GPU):

```python
model_name_fp8 = "zai-org/GLM-5-FP8"
model_fp8 = AutoModelForCausalLM.from_pretrained(
    model_name_fp8,
    torch_dtype="float8_e4m3fn",  # FP8 precision (supported in recent PyTorch)

    device_map="auto"
)

```

## Optimization Strategies for Limited Hardware

For deployments where GPU memory is constrained, the [`example/ascend.md`](https://github.com/zai-org/GLM-5/blob/main/example/ascend.md) file provides additional memory-efficiency tips for large models, including gradient checkpointing and CPU offloading strategies. The `device_map="auto"` parameter utilized in the code examples above automatically distributes model layers across available GPUs and system memory, enabling inference even when individual GPU memory is insufficient.

Additionally, the [`skills/glm-master-skill/SKILL.md`](https://github.com/zai-org/GLM-5/blob/main/skills/glm-master-skill/SKILL.md) file documents that downstream implementations require proper API key configuration (`ZHIPU_API_KEY`), though this primarily affects API-based usage rather than local inference optimization.

## Summary

- **BF16 Precision**: Requires **≥40GB GPU memory** (e.g., NVIDIA A100, A6000)
- **FP8 Quantization**: Requires **≥24GB GPU memory** (e.g., NVIDIA RTX 3090, RTX 4090)
- **Model Architecture**: 744B total parameters with 40B active parameters during inference
- **Key Files**: [`README.md`](https://github.com/zai-org/GLM-5/blob/main/README.md) (lines 63-68) for model specifications, [`example/ascend.md`](https://github.com/zai-org/GLM-5/blob/main/example/ascend.md) for deployment optimizations
- **Implementation**: Use `torch_dtype="bfloat16"` for standard inference or `torch_dtype="float8_e4m3fn"` for quantized deployment

## Frequently Asked Questions

### Can I run GLM-5 inference on a 16GB GPU?

No, the minimum GPU memory requirements for GLM-5 inference exceed 16GB even with quantization. The FP8 variant requires at least 24GB of VRAM. For hardware with less memory, consider using API-based access through the `ZHIPU_API_KEY` referenced in [`skills/glm-master-skill/SKILL.md`](https://github.com/zai-org/GLM-5/blob/main/skills/glm-master-skill/SKILL.md) or implementing CPU offloading strategies.

### What is the difference between GLM-5 and GLM-S?

GLM-S is the "small" variant of the GLM-5 series. As documented in the repository's [`README.md`](https://github.com/zai-org/GLM-5/blob/main/README.md), GLM-S features 744 billion total parameters with an active-parameter footprint of roughly 40 billion during inference, making it the accessible version of the GLM-5 model family.

### Does FP8 quantization affect model accuracy?

The FP8 quantized checkpoint (`GLM-S-FP8`) reduces memory consumption by approximately 50% compared to BF16. While FP8 offers lower precision than BF16, it is designed to maintain competitive performance for inference-only workloads where the 24GB memory requirement is necessary.

### Where can I find deployment guides for non-NVIDIA hardware?

The [`example/ascend.md`](https://github.com/zai-org/GLM-5/blob/main/example/ascend.md) file in the repository contains specific deployment instructions and memory-efficiency optimizations for Ascend NPU hardware, providing alternative deployment strategies for organizations using Huawei AI accelerators.