@TheAhmadOsman: Local AI hardware = capacity × bandwidth × software stack - Capacity tells you what fits - Bandwidth tells you how hard…

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Summary

A detailed comparison of local AI hardware in terms of memory capacity, bandwidth, and software stack, covering GPUs, Apple Silicon, AMD, Intel, Tenstorrent, and others, with a focus on what bottlenecks matter for AI inference.

Local AI hardware = capacity × bandwidth × software stack - Capacity tells you what fits - Bandwidth tells you how hard the box can breathe - The software stack tells you how much of the spec sheet you can actually cash out. Hardware by Memory Bandwidth - Mac Studio M3 Ultra: up to 512GB @ 819 GB/s - RTX PRO 6000 Blackwell: 96GB @ 1792 GB/s - RTX 5090: 32GB @ 1792 GB/s - RTX 4090: 24GB @ 1008 GB/s - RX 7900 XTX: 24GB @ 960 GB/s - Radeon PRO W7900: 48GB @ 864 GB/s - AMD Radeon AI PRO R9700: 32GB @ 640 GB/s - Intel Arc Pro B65: 32GB @ ~608 GB/s - Tenstorrent Wormhole n300: 24GB @ 576 GB/s - Tenstorrent Blackhole p150: 32GB @ 512 GB/s + 800G - MacBook Pro M5 Max: 460-614 GB/s - MacBook Pro M5 Pro: 307 GB/s - DGX Spark: 128GB @ 273 GB/s (coherent + CUDA) - Mac mini M4 Pro: 273 GB/s - Ryzen AI Max / Strix Halo: ~256 GB/s (~96GB usable GPU) - MacBook Air M5: 153 GB/s - Snapdragon X2 Elite: 152-228 GB/s - Intel Lunar Lake: 136 GB/s - Snapdragon X Elite: 135 GB/s - Mac mini M4: 120 GB/s - Arc Pro B60: 24GB @ ~456 GB/s Verdict - GPUs are still the bandwidth kings - Apple wins: stupid amounts of memory, don’t want to shard across GPUs - Apple loses: when raw tokens/sec & concurrency matter more - DGX Spark: coherent memory + NVIDIA stack - Strix Halo / Ryzen AI Max: first real x86 unified-memory contender - Tenstorrent: fully OSS stack, excited to see this mature Fitting ≠ serving Even if it fits, you still pay for - bandwidth during decode - KV cache growth - dequantization - batching + concurrency - scheduler quality - framework overhead The only mental model that matters: 1. What must fit? 2. What bandwidth tier do I need? 3. What software stack can actually deliver it? In short: - NVIDIA → fastest raw speed - Apple Studio M3 Ultra → biggest one-box memory - Strix Halo → first real x86 unified - DGX Spark → coherent NVIDIA dev appliance - AMD / Intel Arc → rising alternatives - Tenstorrent → fully opensource stack Do ask: “which bottleneck am I buying?” Not: “which hardware is best?”
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Cached at: 06/22/26, 03:30 AM

Local AI hardware = capacity × bandwidth × software stack

  • Capacity tells you what fits
  • Bandwidth tells you how hard the box can breathe
  • The software stack tells you how much of the spec sheet you can actually cash out.

Hardware by Memory Bandwidth

  • Mac Studio M3 Ultra: up to 512GB @ 819 GB/s
  • RTX PRO 6000 Blackwell: 96GB @ 1792 GB/s
  • RTX 5090: 32GB @ 1792 GB/s
  • RTX 4090: 24GB @ 1008 GB/s
  • RX 7900 XTX: 24GB @ 960 GB/s
  • Radeon PRO W7900: 48GB @ 864 GB/s
  • AMD Radeon AI PRO R9700: 32GB @ 640 GB/s
  • Intel Arc Pro B65: 32GB @ ~608 GB/s
  • Tenstorrent Wormhole n300: 24GB @ 576 GB/s
  • Tenstorrent Blackhole p150: 32GB @ 512 GB/s + 800G
  • MacBook Pro M5 Max: 460-614 GB/s
  • MacBook Pro M5 Pro: 307 GB/s
  • DGX Spark: 128GB @ 273 GB/s (coherent + CUDA)
  • Mac mini M4 Pro: 273 GB/s
  • Ryzen AI Max / Strix Halo: ~256 GB/s (~96GB usable GPU)
  • MacBook Air M5: 153 GB/s
  • Snapdragon X2 Elite: 152-228 GB/s
  • Intel Lunar Lake: 136 GB/s
  • Snapdragon X Elite: 135 GB/s
  • Mac mini M4: 120 GB/s
  • Arc Pro B60: 24GB @ ~456 GB/s

Verdict

  • GPUs are still the bandwidth kings

  • Apple wins: stupid amounts of memory, don’t want to shard across GPUs

  • Apple loses: when raw tokens/sec & concurrency matter more

  • DGX Spark: coherent memory + NVIDIA stack

  • Strix Halo / Ryzen AI Max: first real x86 unified-memory contender

  • Tenstorrent: fully OSS stack, excited to see this mature

Fitting ≠ serving

Even if it fits, you still pay for

  • bandwidth during decode
  • KV cache growth
  • dequantization
  • batching + concurrency
  • scheduler quality
  • framework overhead

The only mental model that matters:

  1. What must fit?
  2. What bandwidth tier do I need?
  3. What software stack can actually deliver it?

In short:

  • NVIDIA → fastest raw speed
  • Apple Studio M3 Ultra → biggest one-box memory
  • Strix Halo → first real x86 unified
  • DGX Spark → coherent NVIDIA dev appliance
  • AMD / Intel Arc → rising alternatives
  • Tenstorrent → fully opensource stack

Do ask: “which bottleneck am I buying?”

Not: “which hardware is best?”

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