How Nvidia’s GB300 GPU Is Shaping AI Inference and Cloud Supply Chains

GPU Architecture and Performance Gains

The GB300 and B300 GPUs, launched six months after the GB200/B200, are built on TSMC’s 4 nm process and feature a new die‑shot optimized for compute workloads. Compared with the B200, FLOPS increase by 50 % while power draw rises to 1.4 kW (GB300) and 1.2 kW (B300) versus 1.2 kW and 1 kW for the previous generation. Memory capacity grows from 192 GB to 288 GB of HBM, with the stack moving from 8‑layer HBM3E to 12‑layer, keeping bandwidth at 8 TB/s.

FLOPS performance +50 %.

Power consumption up to 200 W.

HBM capacity +50 % (192 GB → 288 GB).

Stack depth 8 → 12 layers.

Impact on Large‑Model Inference

Increased memory benefits models such as OpenAI’s O3, where larger KV‑Cache sizes enable bigger batch sizes and lower latency. Benchmarks show that from H100 to H200, memory bandwidth rises from 3.35 TB/s to 4.8 TB/s, delivering a 43 % average latency improvement across batch sizes and a three‑fold increase in tokens generated per second, effectively cutting inference cost by about three times.

Higher bandwidth → 43 % latency reduction.

Larger batch sizes → 3× token throughput, ~3× cost reduction.

NVL72 Accelerator System

The NVL72 platform, used with GB200 and GB300, connects up to 72 GPUs with ultra‑low‑latency all‑to‑all switched connectivity and integrated all‑reduce capabilities. This unique accelerator enables distributed KV‑Cache across 72 GPUs, extending feasible inference token lengths beyond 100 k tokens and delivering more than ten‑fold economic benefits for long‑chain reasoning tasks.

72‑GPU collaboration with shared memory.

All‑to‑all switched connectivity + all‑reduce.

Supports inference token lengths >100 k.

Economic gain >10× for long‑chain workloads.

Supply‑Chain and Platform Changes

For the GB300, Nvidia abandons the integrated Bianca motherboard and moves to an SXM Puck module that houses the B300 GPU and a Grace CPU in BGA package. The second‑stage memory shifts from soldered LPDDR5X to replaceable LPCAMM modules supplied mainly by Micron. While the switch‑tray and copper backplane remain unchanged, the VRM components are now sourced directly from large‑scale OEMs rather than being integrated on the board.

These changes open the platform to more OEM/ODM partners: previously only Wistron and Foxconn Industrial Internet (FII) could produce Bianca boards; now additional vendors can assemble SXM Puck‑based systems, altering market share dynamics.

Effect on Hyper‑Scale Cloud Providers

All major hyper‑scale clouds (Meta, Google, Microsoft, Oracle, xAI, CoreWeave) have committed to the GB300, attracted by higher FLOPS, larger memory, and greater system customization. However, rapid rollout and the need to redesign racks, cooling, and power density have forced providers like Amazon to adopt sub‑optimal configurations (e.g., 200 G Elastic Fabric Adaptor NICs) that increase total cost of ownership (TCO) compared with Meta or Google’s NVL72 deployments.

Amazon’s current setup limits its ability to use the full NVL72 architecture, but future adoption of water‑cooled designs and upcoming 400 G NICs (expected Q3 2025) could allow a return to NVL72, improving TCO dramatically.

Microsoft appears to be the latest adopter, still procuring GB200 in Q4, indicating a staggered migration timeline across the industry.

Conclusion

Nvidia’s GB300/B300 GPUs deliver substantial compute and memory upgrades that directly benefit AI inference performance, especially for large‑model, long‑chain reasoning. The shift to modular SXM Puck designs reshapes the supply chain, broadening OEM participation and altering competitive dynamics among cloud providers. While the hardware promises cost and latency gains, realizing these benefits depends on each provider’s ability to redesign infrastructure and adopt the NVL72 accelerator ecosystem.