Instructions to use mlx-community/DeepSeek-V3.2-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/DeepSeek-V3.2-8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/DeepSeek-V3.2-8bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/DeepSeek-V3.2-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/DeepSeek-V3.2-8bit" --prompt "Once upon a time"
Any tips on best way to run across 2 x M3 (512GB) Ultras?
There are various ways to utilise the two M3 Ultras - EXO or MLX Distributed - has anyone direct feedback on best approach with some details on the config / setup process?
One thing that comes to mind is the 6 x TB5 ports which can be aggregated.
You can physically connect multiple Thunderbolt 5 ports from Studio A directly to the same number of ports on Studio B. To utilise the combined bandwidth (e.g., 6×120 Gbps=720 Gbps) - theoretically.
This can be configured in macOS.
The real question is, if this going to have a significant effect on the tokens / sec, - my gut feel is that the answers is no since we are most likely compute bound in the first place.
For instance if we have a 4 bit quant running on a single M3 ultra - and are say, getting 20 t/s, then if we have 2 x M3 ultra's and "run" an 8 bit quant across the two - with zero network delay (extreme hypothetical) - we are highly unlikely to get more than 20 t/s for a smaller (more compute) quant.
So what is the minimum network requirement between the two M3's to not bottleneck the compute?
This is an interesting subject .
Has anyone investigated this in the real world - with physical connections / machines?
For scaling up TPS, latency is the bottleneck not bandwidth.
This works just fine with EXO, you can scale single request TPS up to 3.2x with 4 nodes.
We have benchmarked this.