AGGX hardens GPU virtualization, parallelism, and attestation into a programmable substrate for AI. Five layers turn a fleet of heterogeneous GPUs into one elastic, verifiable, and measurable compute grid.
Not a rental marketplace with a dashboard on top — a control plane that decides where every workload runs, proves it ran correctly, and prices it by the second.
Each layer depends only on the one beneath it. That is what makes the grid portable across hardware generations — new silicon slots in at layer 01 without rewriting anything above it.
Heterogeneous GPU fleets — Blackwell, Hopper, and Ampere — wired with NVLink inside the node and InfiniBand or 800 GbE between them. Nodes are pooled into a single logical grid rather than managed as isolated clusters.
Adaptive GPU Grid Xlicing partitions each physical GPU into hardware-isolated units with their own memory ceiling, compute share, and bandwidth floor. Slices are the unit of scheduling, metering, and QoS enforcement.
A telemetry-driven control plane places work across the grid in near real time, matching each job to the topology that fits it. A policy engine enforces reservations, quotas, tenancy rules, and chargeback across teams.
Trusted execution environments produce cryptographic proof that work ran as scheduled at full precision. Every slice emits a signed usage record you can reconcile against your own telemetry.
Optimized runtimes for training and inference sit on top: continuous batching, paged attention, quantized weights, and autoscaled endpoints — with ephemeral slices that spin up in milliseconds for bursty traffic.
Six engineering problems sit between a rack of GPUs and a workload that runs fast, cheaply, and provably. These are the ones we work on.
Partitioning that adapts to the workload, not the other way around.
Static GPU allocation wastes capacity: a 7B inference model pinned to an 80 GB card leaves most of the silicon idle. AGGX slices each GPU into hardware-isolated partitions sized to the job, then resizes the partition map as demand shifts — without evicting running work.
Placement decisions informed by what the hardware is actually doing.
Every node streams continuous telemetry — VRAM pressure, SM occupancy, thermal headroom, NVLink and fabric saturation, matrix-multiplication throughput, and network jitter. The router trains on that stream to predict where a given job will run fastest and cheapest, then places it there.
Proof that the work you paid for actually ran.
In a distributed grid, trust cannot be assumed. AGGX audits distributed workers to confirm they executed full-precision matrix operations as scheduled — catching silent downgrade to lower precision, spoofed results, and partial execution before those results reach you.
Serving models at a fraction of the naive cost.
The fastest GPU is the one you do not have to rent. Our engineering work concentrates on making models cheaper to serve — through quantization, compression, kernel-level tuning, and batching strategies that raise throughput without breaking latency targets.
Scaling past the memory ceiling of a single card.
Frontier models do not fit on one GPU. AGGX handles the sharding, collective communication, and failure recovery that multi-GPU and multi-node execution demand — so a 200B-parameter model behaves like a single addressable endpoint.
Every slice is measured, priced, and explainable.
Slice-level instrumentation feeds both your dashboards and our capacity planner. The same telemetry that drives scheduling also models global GPU availability and power-cost curves, pricing capacity in real time rather than on a fixed rate card.
Workloads are matched to the generation that fits them. Long-context training goes to Blackwell; steady inference runs cheaper on Hopper and Ampere slices.
| GPU | Architecture | Memory | Interconnect | Node Format | Status |
|---|---|---|---|---|---|
| NVIDIA B300 | Blackwell Ultra | 288 GB HBM3e | 1.8 TB/s NVLink | HGX B300 NVL8 | Q4 2026 |
| NVIDIA B200 | Blackwell | 180 GB HBM3e | 1.8 TB/s NVLink | HGX B200 / NVL72 | Deploying |
| NVIDIA H100 | Hopper | 80 GB HBM3 | 900 GB/s NVLink | HGX H100 SXM | Available |
| NVIDIA A100 | Ampere | 80 GB HBM2e | 600 GB/s NVLink | HGX A100 SXM | Available |
Slices present as standard CUDA devices. If your job runs on a normal GPU today, it runs on AGGX without code changes.
We would rather show you the numbers than describe them. Send us a model and a throughput target, and we will run it on the grid and share the full profile — including where it bottlenecks.