Symphony
Single-node platforms that consolidate full AI clusters — up to 64 accelerators, unified DDR5 memory, and zero-hop latency for inference and fine-tuning at production scale.
The platform line that collapses the cluster.
Every I/ONX platform runs the same heterogeneous, host-tax-free architecture — from the rack to the edge. Compose CPUs, GPUs, ASICs, and FPGAs behind a unified memory fabric, and run inference and fine-tuning at production scale.
Symphony SixtyFour
One node. Up to sixty-four accelerators. Zero host tax.
Symphony SixtyFour consolidates an entire multi-node cluster into a single, vendor-neutral node — collapsing the network hops, host overhead, and operational complexity that make legacy AI infrastructure expensive to run.
- Up to 64
- per single node
- CPU · GPU · ASIC · FPGA
- vendor-neutral, heterogeneous
- Up to 6TB
- DDR5, shared address space
- Zero-hop
- single-node, no network fabric
- Inference · Fine-tuning
- production-scale
- Up to 90%
- vs. traditional clusters
Change the architecture and the whole equation moves.
50%
Reduction in total cost of ownership
70%
Reduction in rack-scale deployment cost
75%
Lower power consumption vs. traditional clusters
90%
Reduction in CPU footprint
Synth
Heterogeneous compute engineered for the edge — bringing I/ONX efficiency to deployments where power, space, and mobility are constrained.
The efficiency is in the architecture — not the accelerator.
Vendor-neutral by design
Mix CPUs, GPUs, ASICs, and FPGAs in one node. Never be locked to a single accelerator roadmap or supply chain again.
Single-node, zero-hop
Collapsing the cluster into one node removes network fabric, host overhead, and the latency of every hop between them.
Unified memory
Up to 6TB of shared DDR5 gives every accelerator a common address space — no partitioning, no data-shuffling tax.
Sustainable at scale
Dramatically lower power and cooling means faster ROI and a smaller footprint for every production AI deployment.
Ready to rethink your AI infrastructure?
Tell us about your inference and fine-tuning workloads. We'll show you what I/ONX efficiency looks like on your deployment.