The open-model community learned early that compute is destiny. Diffusion models democratized image generation precisely because GPUs and good tooling became accessible. The same dynamic now plays out one level down, in the physical infrastructure that powers every model, from a local Stable Diffusion checkpoint to a hosted assistant like Chat AI. This piece maps that stack and the companies entering it.
Chips: the accelerator tier
NVIDIA GPUs with NVLink still define training, with AMD's MI accelerators as the merchant alternative. The bigger shift is hyperscalers building their own: Google TPUs, Amazon Trainium and Inferentia (Annapurna Labs), and Microsoft Maia. Owning the silicon lets them tune memory and interconnect around their actual workloads instead of renting someone else's roadmap.
Networking: the cluster fabric
Large training runs are bottlenecked by communication. NVLink and NVSwitch handle the node, InfiniBand or Ultra Ethernet spans the cluster, and optical interconnects extend reach as copper runs out. Broadcom and Marvell supply much of the switching and custom interconnect silicon, with co-packaged optics emerging to cut energy per bit.
Materials and manufacturing
- ASML's EUV lithography is a single-vendor chokepoint.
- TSMC, Samsung, and Intel Foundry turn designs into wafers.
- CoWoS-class advanced packaging gates output because HBM stacks (SK Hynix, Samsung, Micron) must sit next to compute dies.
- Substrates, interposers, and photoresist quietly sit on the critical path.
Power and the electric grid
Electricity is now the gating resource. Frontier campuses request hundreds of megawatts, and grid interconnection queues run for years. Utilities, independent power producers, gas-turbine vendors, and nuclear operators are signing power deals with data center builders, and some operators co-locate next to generation to skip transmission limits entirely.
Cooling
Rack densities have outgrown air. Direct-to-chip liquid cooling is now standard, and immersion is going mainstream, with vendors competing on coolant distribution, cold-plate design, heat reuse, and water/energy overhead.
The software "harness"
Around the metal sits the harness that turns weights into products: AI-native IDEs and coding agents, inference engines like vLLM and TensorRT-LLM, routing gateways, vector databases, evaluation tooling, and agent frameworks. For creative teams, this is the layer that connects a Stable Diffusion pipeline to grounded research, planning, and packaging, much like the orchestration that a multimodal assistant such as Chat-AI performs around generation.
The deals: foundries and fabs
Capacity contracts are the real scoreboard. OpenAI has reportedly worked with Broadcom and TSMC on custom chips; Amazon scales Trainium; Google co-designs TPUs with Broadcom; Intel courts external foundry customers; TSMC expands in Arizona and Japan. These multi-year commitments decide who can field the next generation of models.
The latest inference boards
- Groq — a deterministic LPU with large on-chip SRAM for very low token latency.
- Cerebras — a wafer-scale engine keeping whole models on one chip.
- Etched — a transformer-specialized ASIC (Sohu) built for throughput.
- Taalas — etching specific models more directly into silicon for efficiency.
Why image-first teams should care
Cheaper, faster inference reshapes what creative pipelines can afford. As specialized boards drop the cost of serving, grounded multimodal assistants like Chat AI become viable upstream of Stable Diffusion for research and planning, and the open-model ecosystem benefits from the same infrastructure tailwinds that the frontier labs are building.