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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchYou can run Qwen 3.5 locally on an Apple Silicon Mac with MLX-LM: install the package, start a server with a compatible model checkpoint, then send requests to its local OpenAI-compatible endpoint. The setup is straightforward; a universal “2× faster” claim is not established. Speed depends on the specific Mac, checkpoint, quantization, runtime, workload, and comparison baseline.
What you need before installing
- An Apple Silicon Mac with enough unified memory for your chosen model and workload. Model weight size alone is not a complete memory requirement: context length and runtime overhead also matter.
- Python and pip available in your environment. The setup below follows Apple’s WWDC26 MLX-LM example; consult the current model card and package documentation if commands or compatibility have changed.
- A checkpoint that works with the runtime you plan to use. Model identifiers and runtime instructions are not interchangeable: Apple’s server demo uses a 4B checkpoint with MLX-LM, while the cited 9B vision-language conversion documents use with MLX-VLM.
Apple’s example installs MLX-LM and starts a local server with mlx-community/Qwen-3.5-4B-8bit. See Apple’s WWDC26 local-agent walkthrough for that workflow. Check that the exact checkpoint remains available and compatible before using it.
Install and start the MLX-LM server
- Install MLX-LM: in a terminal, run
pip install mlx-lm. Use the Python environment in which you intend to run the server. - Start the example server: run
mlx_lm.server --model mlx-community/Qwen-3.5-4B-8bit. The model must be downloaded the first time it is used, so allow for network access and disk space. - Leave the server running: Apple’s example exposes it on the local address
http://127.0.0.1:8080. - Send a chat-completions request: use the endpoint
http://127.0.0.1:8080/v1/chat/completionsand set the request’s model name todefault_model, as in Apple’s example. Your client must be configured to use this local base URL rather than a hosted API.
The server command’s checkpoint name is significant. Do not replace it with a similarly named model without checking that the checkpoint is supported by MLX-LM and by the server interface.
Choosing a Qwen 3.5 checkpoint and runtime
Apple’s 4B MLX-LM server example
The MLX-LM server workflow above uses mlx-community/Qwen-3.5-4B-8bit. It is the clearest documented path here for serving a local model to a client that speaks the OpenAI chat-completions format.
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A separate 9B vision-language conversion
The MLX Community Qwen3.5-9B-MLX-8bit model card describes an 8-bit SafeTensors conversion of Qwen/Qwen3.5-9B for Apple Silicon, with group size 64. The card lists a 10.4 GB repository/model size and documents Python and command-line use through MLX-VLM. That figure describes the listed checkpoint, not the Mac’s total memory requirement; downloads also need storage, and runtime use adds memory needs. The card notes a more optimized conversion may be available, so check its current recommendations.
Do not substitute this 9B MLX-VLM checkpoint into the MLX-LM server command by changing only the model name. The two examples use different runtimes and checkpoint identifiers; compatibility for a particular server setup must be verified.
How much memory does a Mac need?
There is no complete model-size-to-Mac-memory compatibility chart in the cited sources. The 9B card’s 10.4 GB figure is a checkpoint size, not a minimum unified-memory specification. In a different, larger-model example, Ollama says its Qwen3.5-35B-A3B preview workflow requires more than 32 GB of unified memory. That is guidance for that specific preview setup, not a general requirement for every Qwen 3.5 model or MLX-LM installation. See Ollama’s MLX preview notes.
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Choose a checkpoint for the memory available on your Mac, leaving room for the operating system, the runtime, and the context you intend to process. The available figures do not establish which model sizes will fit every Apple Silicon configuration.
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Does MLX make Qwen 3.5 twice as fast?
Not as a general, verified result. A “2×” claim is meaningful only with a named baseline and a controlled comparison. Apple’s cited demonstrations do not measure Qwen 3.5 single-Mac inference against another runtime under identical settings.
For context, Apple’s WWDC26 distributed-MLX session reports around 180 tokens per second for Qwen 3.5 9B fine-tuning on a single M3 Ultra and around 600 tokens per second on a four-Mac cluster. It also reports nearly three times the token generation rate for Qwen 3.6 inference on four Macs versus one M3 Ultra. These are different model/workload and hardware comparisons—not evidence that MLX makes Qwen 3.5 inference twice as fast on one Mac. The demonstrations are described in Apple’s distributed inference and training session.
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Ollama’s March 30, 2026 post describes tests conducted March 29, 2026 with Qwen3.5-35B-A3B, comparing an NVFP4 configuration with an earlier Ollama implementation using Q4_K_M. The post describes Ollama on Apple Silicon as MLX-powered and in preview, and also notes an Ollama 0.19 result with int4 quantization. Since the implementation and quantization differ, that comparison does not isolate an MLX speedup with all settings held constant.
What a fair speed comparison must report
- Mac chip and unified memory.
- Exact checkpoint and revision, plus quantization and weight format.
- Runtime and package versions.
- Prompt and context length, generated-token count, warm-up procedure, and number of repeated runs.
- Whether the metric is time to first token or decode tokens per second.
- Whether the task is inference or fine-tuning.
The cited sources do not provide an apples-to-apples Qwen 3.5 single-Mac test covering these variables.
What “local” means for privacy
With the server and client configured as in Apple’s example, prompts sent to 127.0.0.1 go to a server on the same Mac for inference rather than to a model API. That describes the model-request path, not every part of an application: a third-party agent, plugin, tool, or connected service may send data elsewhere. Check those services’ own data-handling behavior if you need to keep a workflow on-device.
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