AI experiments
Use 64GB of unified memory to test models in your workflow.
ArmRDP M4 Pro comes with an M4 Pro chip, 64GB of unified memory, and a 2TB SSD. Use a dedicated environment to load local models, compare quantized versions, and test inference APIs before deciding whether to invest further.
Test capacity before speed
Model file size isn’t the same as runtime memory use. Account for KV cache, context length, the inference framework, and other processes in addition to model weights. Start with a short context for your target model, record peak memory use and memory pressure, then increase request volume gradually.
Choose an inference framework that supports Apple silicon, and check compatibility with your specific operators and model format. Unified memory doesn’t guarantee that every model will run, and it doesn’t tell you how fast generation will be. Test with your own prompts, context, and output lengths.
Log at least four things for each experiment
- Model name, version, and quantization format.
- Context length, batch size, and peak memory use.
- Initial load time and response time during steady-state operation.
- Request samples, output quality, and failure logs.
Schedule inference experiments separately from production release builds where possible, so memory contention doesn’t skew results. Protect remote APIs with authentication and access controls; don’t expose listening ports publicly.