Collaborative Computing Between Laptops | Generated by AI

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Question: How can two laptops or computers collaborate to do tasks together?

Answer:

There are several levels of collaboration, from simple file sharing to distributed computing.

1. Remote control (one computer controls another)

The simplest model:

Laptop A  --->  Laptop B
          SSH / Remote Desktop

Examples:

SSH (Linux/macOS/Windows)

Laptop A:

ssh user@laptop-b-ip

Then you can run commands on B:

python train.py

The computation happens on B.

For AI development this is common:

Your laptop
    |
    | ssh
    v
GPU workstation
    |
    v
training / inference

2. Share files and code

Use Git:

Computer A
   |
   | git push
   v
GitHub/GitLab
   |
   | git pull
   v
Computer B

Example:

Computer A:

git add .
git commit -m "experiment"
git push

Computer B:

git pull

Both machines have the same code.

3. Split a workload (distributed computing)

For AI training:

              PyTorch Distributed

        +----------------+
        |  Master node   |
        |   Laptop A    |
        +----------------+
              |
       network (NCCL/Gloo)
              |
        +----------------+
        | Worker node    |
        |   Laptop B    |
        +----------------+

Example:

Two GPUs:

GPU0 -> Computer A
GPU1 -> Computer B

PyTorch:

torch.distributed.init_process_group(
    backend="nccl"
)

Run:

Computer A:

torchrun \
 --nproc_per_node=1 \
 --nnodes=2 \
 --node_rank=0 \
 train.py

Computer B:

torchrun \
 --nproc_per_node=1 \
 --nnodes=2 \
 --node_rank=1 \
 train.py

The model training is synchronized.

4. Use one as a server

Very common for engineers:

Laptop
   |
   | API request
   |
   v
Desktop with GPU

RTX 4070 / MI50 / H100

Example:

Computer B:

vllm serve qwen-model

Computer A:

requests.post(
 "http://computer-b:8000/v1/chat/completions"
)

Your laptop becomes the “terminal”; GPU machine does the heavy work.

5. Kubernetes / cluster style

More advanced:

             Kubernetes Cluster

       +----------------+
       | Control Plane  |
       | Computer A    |
       +----------------+

          |
    ----------------
    |              |
Computer B     Computer C
 GPU worker    GPU worker

Jobs are scheduled automatically.

6. Shared folder

Simple home lab:

Computer A
      \
       \
      NAS / Shared Storage
       /
      /
Computer B

Using:

Example:

mount server:/data /mnt/data

Both computers see the same files.

For your AI workstation situation (RTX 4070 + possible AMD MI50), a practical setup would be:

                 Network

MacBook/Laptop
      |
      | SSH
      |
      v
AI Desktop
      |
      |
 RTX 4070
 MI50

Laptop:

Desktop:

Then later:

Machine A RTX 4070
        +
Machine B MI50
        +
Machine C cloud GPU

        |
        v

Distributed PyTorch training

This is basically how small AI labs grow: start with 2 machines, then turn them into a mini cluster.

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