M1→M5 memory upgrade reduces swap stress
A developer reported upgrading an M1 Pro system to an M5 Pro with memory from 16GB to 48GB and observed no‑swap performance gains—illustrating memory, not CPU, as a growing bottleneck for local ML workflows. The thread underscores the practical impact of unified memory sizing on on‑device LLM inference. (x.com)
The M1 Pro 14‑inch shipped with 16GB of unified memory as the base configuration in 2021, and that 16GB baseline remains the reference point for early Apple‑Silicon ML workflows. (support.apple.com)) Apple’s M5 Pro increases unified memory capacity to as much as 64GB and advertises up to 307 GB/s of unified memory bandwidth on Pro configurations. (apple.com)) Apple’s product messaging states the M5 family delivers up to 4× AI throughput versus the previous generation and up to 8× AI performance compared with M1 models, citing neural accelerators and higher memory bandwidth as key enablers. (apple.com)) Systems researchers profiling on‑device inference identify unified memory footprint and bandwidth as leading constraints for LLM latency and throughput, noting that memory pressure—not raw CPU FLOPS—often drives performance regressions. (arxiv.org)) OS‑level analyses document that page faults and swapping introduce millisecond‑scale stalls during LLM inference and that avoiding disk swap improves predictability for streaming and low‑latency generation workloads. (eunomia.dev)) Apple’s own MLX benchmarks include M5 runs using 24GB unified memory for Qwen models, while independent reports have demonstrated experimental workflows (including flash‑backed streaming) that let researchers run extremely large parameter sets on machines with 48GB of unified memory. (machinelearning.apple.com)) Community buying decisions are already reflecting this tradeoff—users weigh M5 Pro configurations with 48GB of unified memory against M5 Max SKU choices—while open‑source tooling notes that only roughly 70–78% of unified memory is typically available to GPU/device allocations, reducing the effective working set for on‑device inference. (forums.macrumors.com))
