Apple Silicon can load 70B parameter models but inference takes 10 seconds per response — useful for nothing except bragging rights

Your M2 Max MacBook Pro has 96GB of unified memory. You load Llama 3 70B Q4_K_M via llama.cpp. It fits! You send a prompt. 15 seconds later, the first token appears. Tokens trickle in at 5-8 tokens per second. A 200-token response takes 25-40 seconds. During generation, your laptop fans spin at full speed, the keyboard gets hot, and everything else on the machine becomes sluggish because the LLM is consuming 90% of memory bandwidth. You cannot run an agentic workflow that requires 20-50 LLM calls because total response time would be 8-30 minutes. So what? Apple Silicon's unified memory architecture was supposed to democratize large model inference — finally, a consumer device with 96-192GB of memory. But memory bandwidth is the bottleneck, not memory capacity. The M2 Max has 400 GB/s bandwidth vs an H100's 3.35 TB/s — 8.4x slower. Inference speed scales linearly with bandwidth for large models (memory-bandwidth-bound). So Apple Silicon will always be 8x slower than datacenter GPUs for large model inference, no matter how much memory Apple adds. Loading the model is impressive but using it is painfully slow. Why does this persist? Apple designs chips for consumer workloads (video editing, browsers, apps) where memory bandwidth of 400 GB/s is more than sufficient. They will not design for AI inference because the market is too small to justify the silicon area for wider memory buses. HBM (High Bandwidth Memory, used in H100) provides 3+ TB/s but is physically incompatible with Apple's package design and would double the chip cost.