Run DeepSeek R1 or V3 with MLX Distributed

mlx_distributed_deepseek.md

Setup

On every machine in the cluster install openmpi and mlx-lm:

conda install conda-forge::openmpi
pip install -U mlx-lm

Next download the pipeline parallel run script. Download it to the same path on every machine:

curl -O https://raw.githubusercontent.com/ml-explore/mlx-examples/refs/heads/main/llms/mlx_lm/examples/pipeline_generate.py

Make a hosts.json file on the machine you plan to launch the generation. For two machines it should look like this:

[
  {"ssh": "hostname1"},
  {"ssh": "hostname2"}
]

Also make sure you can ssh hostname from every machine to every other machine. Check-out the MLX documentation for more information on setting up and testing MPI.

Set the wired limit on the machines to use more memory. For example on a 192GB M2 Ultra set this:

sudo sysctl iogpu.wired_limit_mb=180000

Run

Run the generation with a command like the following:

mlx.launch \
  --hostfile path/to/hosts.json \
  --backend mpi \
  path/to/pipeline_generate.py \ 
  --prompt "What number is larger 6.9 or 6.11?" \
  --max-tokens 128 \
  --model mlx-community/DeepSeek-R1-4bit

For DeepSeek R1 quantized in 3-bit you need in aggregate 350GB of RAM accross the cluster of machines, e.g. two 192 GB M2 Ultras. To run the model quantized to 4-bit you need 450GB in aggregate RAM or three 192 GB M2 Ultras.

You can run a 4-bit quantized model on the 512GB machine. 8-bit is too big.

You can run a 4-bit quantized model on the 512GB machine. 8-bit is too big.

If i hvae 4 such Mac Studios, each with 512GB, how can i run the 8-bit model distributed on the 4 machines?

• If each machine has 512 you only need 2 of them (and I wouldn't recommend using more because it will be slower)
• The above setup should work.. though we'd need to make an 8-bit quant for that.

You can make the 8-bit quant like so once ml-explore/mlx-lm#32 lands.

mlx_lm.convert --hf-path deepseek-ai/DeepSeek-R1 -q --q-bits 8 --upload-repo mlx-community/DeepSeek-R1-8bit

• If each machine has 512 you only need 2 of them (and I wouldn't recommend using more because it will be slower)
• The above setup should work.. though we'd need to make an 8-bit quant for that.

Why does 4 machines slower than 2 machines? Because the connection speed of Thunderbolt 5 is too slow? I thought that more machines mean more kvcache space and it would be faster.

With pipeline parallelism (which is used here).. if you have enough RAM to fit the model then you only are adding communication latency as you add more machines.

With pipeline parallelism (which is used here).. if you have enough RAM to fit the model then you only are adding communication latency as you add more machines.

Does mlx-lm support tensor parallelism?

Sort of. There is a PR for it.. but it's too slow right now to be practical. Something we are working on / hoping to improve for the future.

404 for the url
https://raw.githubusercontent.com/ml-explore/mlx-examples/refs/heads/main/llms/mlx_lm/examples/pipeline_generate.py

The run script can be downloaded at
https://github.com/ml-explore/mlx-lm/blob/main/mlx_lm/examples/pipeline_generate.py

Good News

pipeline_generate.py work very well with other DeepSeek model "DeepSeek-V2.5-1210-3bit "

mlx.launch --hosts mac1,mac2 --backend mpi "pipeline_generate.py" --max-tokens 12800 --model mlx-community/DeepSeek-V2.5-1210-3bit --prompt "Generate a python script"

==========
Prompt: 21 tokens, 85.378 tokens-per-sec
Generation: 776 tokens, 17.794 tokens-per-sec
Peak memory: 55.234 GB

mlx.launch --hosts mac1,mac2 --backend mpi "pipeline_generate.py" --max-tokens 12800 --model mlx-community/DeepSeek-V2.5-1210-4bit --prompt "Generate a python script"

==========
Prompt: 21 tokens, 80.473 tokens-per-sec
Generation: 901 tokens, 17.410 tokens-per-sec
Peak memory: 70.257 GB

Less good News

1°) When I run mlx_distributed_deepseek.py
error message :

except statement is broken in "distributed_run.py"

Edit around line 175. Find:
in the file "except e:"
replace with
"except Exception as e:"

2°) And when I run this command: mlx.distributed_config --verbose --hosts
error message :

/miniconda3/envs/mlxmpi/lib/python3.11/site-packages/mlx/distributed_run.py", line 507, in prepare_tb_ring
connected_to = items[0]["domain_uuid_key"]
~~~~~~~~^^^^^^^^^^^^^^^^^^^
KeyError: 'domain_uuid_key'

Does mlx support gguf format?