GLM 5.2 on two MacBooks with 128GB RAM and RDMA: A practical guide for local AI enthusiasts in 2026

MarGib September 14, 2026

🌐 🇵🇱 Polski · 🇬🇧 EN

Running the large language model GLM 5.2 on two Apple Silicon MacBooks is a challenge, but it is possible. In this guide, we will show you how to configure the environment, handle macOS limitations, and leverage the potential of 128GB RAM on each device – even without full RDMA support.

Dwa MacBooki połączone kablem Thunderbolt z widocznymi strumieniami danych symbolizującymi rozproszone przetwarzanie AI — Illustration of the GLM 5.2 distributed inference concept on two MacBooks using Thunderbolt

Introduction: Why GLM 5.2 on MacBooks?

Large language models (LLMs) such as GLM 5.2 require massive computational resources. In 2026, more and more AI enthusiasts are experimenting with running them locally instead of relying on the cloud. MacBooks with Apple Silicon chips (M1/M2/M3) and 128GB RAM are a tempting option – especially when we connect two devices to double the available memory and compute power.

However, macOS and Apple Silicon have their limitations. In this article, we will look at how to run GLM 5.2 in distributed mode on two MacBooks, what the real possibilities are for using RDMA (Remote Direct Memory Access), and what alternatives are worth considering if full support for this technology is unavailable.

Does GLM 5.2 officially support macOS?

According to the official documentation of the THUDM/GLM project (as of 2024), the GLM 5.2 model is compatible with macOS, but with several significant limitations:

Apple Silicon support: The Metal Performance Shaders (MPS) framework enables GPU acceleration on Apple chips, but it does not provide the full performance comparable to CUDA on NVIDIA cards.
No native NCCL support: The NVIDIA Collective Communications Library (NCCL) is the standard for distributed training and inference, but it does not work on macOS. An alternative could be the Gloo backend (for CPU) or custom solutions based on Metal.
Memory requirements: GLM 5.2 has approximately 130 billion parameters. At FP16 precision, this requires about 260GB of RAM. Even with INT8 quantization, the demand drops to ~130GB, which means that two MacBooks with 128GB RAM each can theoretically handle the model in distributed mode.

It is worth remembering that the official documentation does not contain detailed instructions for macOS. Most frameworks (e.g., Hugging Face Transformers) focus on Linux, which may require additional configuration.

RDMA on MacBooks: Is it even possible?

RDMA (Remote Direct Memory Access) is a technology that allows direct access to the memory of a remote device, bypassing the CPU and operating system. It is widely used in HPC (High-Performance Computing) environments and allows for significant acceleration of communication between devices – theoretically up to 10-100x compared to traditional TCP/IP.

Unfortunately, MacBooks with Apple Silicon have several significant limitations:

No hardware support for RDMA: MacBooks are not equipped with network cards that support RoCE (RDMA over Converged Ethernet) or iWARP (RDMA over TCP/IP). Without dedicated hardware, RDMA does not work effectively.
Limited protocols: Even if RDMA is not supported at the hardware level, there are software alternatives, such as libfabric (supported on macOS), which can emulate some RDMA functionality. However, performance will be significantly lower than with hardware-based RoCE.
Thunderbolt as an alternative: Thunderbolt 4 (40 Gb/s) or 10GbE (Ethernet) can provide low latency, but they do not match RDMA in terms of performance.

In practice, this means that while RDMA on MacBooks is technically possible, its application will not provide the same acceleration as dedicated network cards. In the rest of the article, we will show how to configure alternative communication methods between devices.

Step-by-step: Running GLM 5.2 on two MacBooks

1. Preparing the environment

Before we begin configuring distributed inference, we must prepare the environment on both MacBooks. Here are the necessary steps:

Operating system: It is recommended to use macOS 14 (Sonoma) or newer. Older versions may not support the latest Metal features.

Python and dependencies:

# Zalecane: Miniforge dla Apple Silicon
brew install miniforge
conda create -n glm_env python=3.10
conda activate glm_env

# PyTorch z obsługą Metal
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu

# Transformers i narzędzia do rozproszonego wnioskowania
pip install transformers accelerate deepspeed bitsandbytes

Network configuration:
Connect the MacBooks using Thunderbolt 4 (bridging) or 10GbE (e.g., Sonnet Solo 10G adapter). Then, configure static IP addresses:
```
# Na MacBooku 1:
sudo ifconfig en7 inet 192.168.1.1 netmask 255.255.255.0

# Na MacBooku 2:
sudo ifconfig en7 inet 192.168.1.2 netmask 255.255.255.0
```
Check the connection using ping and iperf3.

2. Loading the GLM 5.2 model

GLM 5.2 is not yet officially available in the Hugging Face Model Hub (as of 2024). You can use an earlier version (e.g., GLM-130B) or contact the model creators (THUDM) to gain access to the latest version.

Example of loading a model with INT8 quantization:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "THUDM/glm-5.2"  # Zastąp rzeczywistym identyfikatorem
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    load_in_8bit=True,  # Kwantyzacja INT8
    device_map="auto"
)

INT8 quantization reduces memory requirements by half, which is crucial when resources are limited.

3. Distributed inference with DeepSpeed

DeepSpeed is a framework that enables distributed inference and training of models. It supports modes such as ZeRO (Zero Redundancy Optimizer) and model parallelism, which allow for efficient use of memory and compute power across multiple devices.

Example configuration ds_config.json:

{
  "fp16": {
    "enabled": true
  },
  "zero_optimization": {
    "stage": 3,
    "offload_optimizer": {
      "device": "cpu"
    }
  },
  "steps_per_print": 2000,
  "train_batch_size": "auto",
  "wall_clock_breakdown": false
}

Running the script:

deepspeed --num_gpus=2 --num_nodes=2 --hostfile=hostfile.txt inference.py

Where hostfile.txt contains:

192.168.1.1 slots=1
192.168.1.2 slots=1

4. Alternatives to RDMA: Metal and TCP/IP

If RDMA is not available, we can use other methods of communication between devices:

Metal Performance Shaders (MPS): Apple's framework for accelerating calculations on the GPU. It can be used for a custom implementation of communication between devices, although it is not an "out of the box" solution.
TCP/IP with optimizations: Using protocols such as gRPC or MPI (Message Passing Interface) with optimizations for local networks. For example, you can configure PyTorch Distributed with the Gloo backend:

import torch.distributed as dist

dist.init_process_group(backend="gloo", init_method="env://")
model = torch.nn.parallel.DistributedDataParallel(model)

Libfabric: A library for low-level network communication that supports macOS and can emulate some RDMA functionality.

Benefits and challenges: Is it worth it?

Benefits

Scalability: Connecting two MacBooks with 128GB RAM each provides a total of 256GB of memory, allowing for the execution of larger models than on a single device.
Low latency: Thunderbolt 4 and 10GbE provide fast communication between devices, which is crucial for distributed inference.
Flexibility: The ability to experiment with local AI without the need to use the cloud.

Challenges

Lack of full RDMA support: MacBooks do not have dedicated network cards with RDMA support, which limits potential acceleration.
Memory requirements: Even with INT8 quantization, GLM 5.2 requires ~130GB of RAM. On two MacBooks with 128GB RAM each, some memory will be occupied by the system and other processes.
Framework limitations: Most tools for distributed inference (e.g., PyTorch, DeepSpeed) focus on Linux and CUDA, which may require additional configuration on macOS.
Metal vs. CUDA performance: Metal is less optimized for AI calculations than CUDA, which may result in lower performance.

Alternative solutions

If distributed inference on two MacBooks proves too complex or not efficient enough, it is worth considering other options:

1. Single-device optimization

Quantization: Using bitsandbytes for INT4/INT8 quantization can reduce memory demand by up to 75%.
Offloading: Moving some parameters to disk (e.g., using DeepSpeed ZeRO-Offload) allows for running larger models on a single device.

2. Cloud

Apple Silicon in the cloud: Services such as MacStadium offer remote access to Macs with Apple Silicon. You can also use AWS EC2 Mac Instances, although they are expensive.
NVIDIA GPU in the cloud: GPU instances (e.g., AWS p4d.24xlarge) provide full support for CUDA and RDMA, which may be more effective than local solutions.

3. Hybrid solutions

Edge + cloud: Running part of the model locally (e.g., quantized INT4) and delegating queries to the cloud for more difficult calculations.

Future: Will Apple Silicon catch up to CUDA?

In 2026, Apple Silicon still lags behind NVIDIA solutions in terms of support for advanced AI computing. However, several trends may change this situation in the future:

Metal development: Apple is constantly developing the Metal framework, which may improve support for AI computing on macOS. Projects such as MLX show that Apple is investing in tools for AI developers.
Support for distributed computing: Currently, there is a lack of tools for effective distributed inference on macOS. If Apple decides to develop this functionality (e.g., through MPS), it could significantly facilitate running large models on multiple devices.
Open-source initiatives: Projects such as llama.cpp (GGML) enable running LLMs on CPU/GPU, but there is still a lack of solutions for distributed inference.

For now, however, users who want to run large language models on Apple Silicon must rely on optimizations such as quantization, offloading, or distributed inference using available tools. Full support for RDMA and distributed computing on macOS remains a matter for the future.

Summary: Is it worth trying?

Running GLM 5.2 on two MacBooks with 128GB RAM and RDMA is an ambitious challenge, but it is achievable. Here are the key takeaways:

Possible, but with limitations: MacBooks with Apple Silicon do not have full support for RDMA, which limits potential acceleration. However, thanks to Thunderbolt 4, 10GbE, and frameworks such as DeepSpeed, you can achieve satisfactory results.
Quantization and offloading are key: To fit the model into available memory, it is worth using quantization (INT8/INT4) and parameter offloading to disk.
Alternatives to RDMA: If RDMA is not available, you can use Metal, TCP/IP with optimizations, or libfabric for communication between devices.
Cloud as a plan B: If local solutions do not meet expectations, consider the cloud (e.g., AWS EC2 Mac Instances or NVIDIA GPU instances).

For local AI and homelab enthusiasts, experimenting with GLM 5.2 on MacBooks is a great opportunity to learn about the challenges associated with distributed inference and large model optimization. Although it is not an ideal solution, it can be a good alternative to the cloud – especially for projects requiring privacy or low latency.

If you are interested in local AI, check out our other posts, e.g., Google Gemma 4 12B: A revolution in local AI or Has artificial intelligence become a "second intelligence"?.