I/ONX
§ R — Resources

The evidence behind heterogeneous compute.

White papers, third-party validation reports, company news, and a technical glossary on heterogeneous compute, kernel fusion, and composable AI infrastructure — the depth behind the numbers.

§ R.3 — Glossary

The language of composable AI infrastructure.

Heterogeneous compute
Running a workload across a mix of processor types — CPU, GPU, ASIC, FPGA — inside one system, matching each job to the right silicon.
The Host Tax
The redundant CPUs, memory, networking, and cooling a legacy GPU node carries only to feed its accelerators — as much as 30kW of wasted power per rack.
Kernel fusion
Combining multiple compute operations into a single kernel to cut memory traffic and latency, so accelerators spend more time computing.
Composable AI infrastructure
Infrastructure whose compute, memory, and accelerators can be assembled per workload rather than fixed in rigid nodes.
Unified memory
A shared address space — up to 6TB of DDR5 — that every accelerator in the node can reach without partitioning or data-shuffling overhead.
Zero-hop latency
Collapsing the cluster into a single node removes the network fabric, so data never traverses a hop between accelerators.
§ R.4 — Podcasts & Interviews

Conversations on AI acceleration.

Interviews, podcasts, and webinars on heterogeneous compute and where the industry is heading.

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§ 09 — Contact

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