Forget LiDAR ?
J’espère que c’est une blague
Pendant que certains enterrent le LiDAR parce qu’ils pensent encore uniquement à la voiture autonome, STMicroelectronics $STM vient de sortir une brique que toute l’industrie robotique attendait
Cette brique s’appelle VL53L9
Et ce n’est pas un LiDAR “à l’ancienne” : gros, cher, complexe, difficile à intégrer
C’est le premier module 3D LiDAR direct
Concrètement :
STM donne à un robot compact la capacité de voir son environnement en 3D, en temps quasi réel, sans lui coller une usine à gaz sur la tête
Un robot doit comprendre l’espace, détecter les obstacles, mesurer les distances, faire du SLAM, éviter les collisions, repérer les petits objets, tout ça avec des contraintes brutales de taille, consommation, latence, intégration et surtout fiabilité
Et ça s’est directement reflété dans le prix :
>STM a fait +100% depuis qu’on en a parlé
Car le marché re-rate STM comme LA boîte européenne qui bénéficiera grandement des robots humanoïdes et du Physical AI
Local AI hardware = capacity X bandwidth X 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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