Apple's biggest bottleneck for real-world inference is prefill processing. They need a better GPGPU architecture, which is what I'm expecting M7 to reveal.
replyAgreed, seems like the M5 has already made steps in that direction, with 4x prompt processing / prefill performance vs. M4. [1] And token generation also got a 10% boost. The data below is only for Pro & Max but I think the base M5 got the same relative boosts vs. M4 base.
reply Chip BW (GB/s) GPU Cores Q4_0 Prompt Q4_0 Gen
M4 Pro (20c) 273 20 439.78 50.74
M4 Max (40c) 546 40 885.68 83.06
M5 Pro (20c) 307 20 ~1500 to 1700 ~56
M5 Max (40c) 614 40 ~3000 to 3500 ~92
ELI5?
replyLook at the "Q4_0 Prompt" column. That's the tokens-per-second processing the initial prompt/system prompt. This is where the "neural accelerators inside each GPU core" is seen most prominently.
replyThe M5 GPU added better matmul and dot product support in hardware (IIRC) that really boosted performance of those kernels over previous GPUs. Current transformer models rely heavily on matmuls. Prefill (processing the current context) is mostly limited by raw GPU processing power where token generation (predicting the next token from the current context) is mostly limited by memory bandwidth.
replyWhile an older M-series might run a matmul or dot product kernel just fine the M5 can run them much faster.
Apple's biggest bottleneck for real-world inference is prefill processing.
LPDDR6 is coming.
replyI understand that will boost read rates to around 14 Gbps as compared to the current 10 Gbps for LPDDR5X, so a 40% improvement.
replyThe bus is also 50% wider so the bandwidth is double.
replyOr they could make use of LPDDR5X-PIM? That would be such a killer feature and competitive advantage.
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