SCOI Atlas · L0 Infrastructure
L0A Silicon & Memory
Accelerators (Nvidia, AMD, TPU, Trainium, custom), HBM, and the memory hierarchy that dominates training cost.
TL;DR · Direct answer
L0A Silicon & Memory is a sublayer of L0 Infrastructure in the Supply Chain of Intelligence™ (SCOI) by Anand Arivukkarasu. It is a required capability but not on its own a durable moat. Your inference stack should assume accelerator diversity within 24 months — architect the abstraction now.
What actually matters at L0A
- HBM supply, not GPU dies, has been the true 2024–2026 bottleneck.
- FP8 and lower-precision training changes the memory-to-compute ratio and re-rates hardware choices.
- Custom silicon economics only work above ~$500M/yr of inference spend.
The startup lens
Your inference stack should assume accelerator diversity within 24 months — architect the abstraction now.
Vertical lens — how this plays across categories
Vertical AI SaaS
Use inference routers (portkey, openrouter, in-house) to survive supplier shifts.
Consumer AI apps
Design for on-device inference where latency and cost matter.
Enterprise AI platforms
Offer BYO-compute so regulated customers can bring reserved capacity.
How to defend L0A
- Multi-accelerator support built into the runtime.
- Contracts with unit-economics review clauses tied to price/token, not price/GPU-hour.
Other sublayers in L0 Infrastructure
L0B
Data Centers
Powered shells, colo, hyperscale — the physical containers of intelligence.
L0C
Interconnect Fabric
NVLink, InfiniBand, Ethernet-based scale-out — the pipes between accelerators.
L0D
Compute & State Infrastructure
Vector DBs, object stores, feature stores, KV caches — persistent state around ephemeral compute.
L0E
Edge & On-Device Compute
NPUs in phones, laptops, cars, wearables and industrial devices.