A GPU that waits on data still bills by the hour — in an AI datacenter or a neocloud alike. The zx Appliance feeds clusters at line rate and moves checkpoints, weights, and datasets ~10× faster than typical movers — so your AI data pipelines keep flowing and your most expensive hardware stays at work.
zx runs at line rate and scales out with no software ceiling — keeping accelerators continuously fed instead of waiting on storage.
Shuttle multi-terabyte checkpoints and weights between storage, clusters, and regions at ~10× the speed of typical movers — so a save or restore is no longer a coffee break.
Move file and object across edge, hybrid, and cloud. The cloud penalty runs 30–50% on a naive path; a co-designed path avoids it, so you stage where the GPUs are without paying the tax.
A pretuned, system-engineered appliance — hardware matched to zx, no transfer-tuning project, no science experiment. Rack it and hit the numbers the first day.
zx embeds directly in NVIDIA BlueField DPUs — moving data at line rate while offloading it from the host CPU. That sidesteps the 30–50% penalty of standard cloud and host data paths, and does it with fewer servers and less power. Featured on the NVIDIA blog; demonstrated at SC21 and SC22.
Data movement runs on the DPU, not the server — so host cores stay on the work that feeds your GPUs.
A co-designed, DPU-resident path avoids the 30–50% overhead of HTTP/REST cloud tooling — line rate, in and out.
More throughput per watt and per rack unit — the efficiency and TCO win that compounds across your fleet.
NVIDIA's blog highlighted that Zettar's BlueField-3 data-migration and storage-offload solution consolidates into about 4U of rack space — versus roughly 13U for an x86-based equivalent.
"Zettar moved an actual petabyte over a 5,000-mile network loop in 29 hours — encrypted and checksummed — at 96% bandwidth utilization."
That run was capped at 80 Gbps to spare the shared network — on a full 100 Gbps link, it's a petabyte a day.
The bottleneck is usually data movement, not compute — datasets, checkpoints, and weights arriving too slowly. Move them in parallel at line rate with a scale-out data mover so the pipeline keeps up; the Zettar zx Appliance feeds clusters at wire speed with no software ceiling.
Idle GPUs usually stall on data that has not arrived. Zettar moves datasets, checkpoints, and model weights at wire speed so accelerators stay fed and keep earning.
Moving the large datasets, model checkpoints, and weights that AI training and inference depend on — across storage, sites, and clouds — fast enough to keep GPU clusters busy.
At line rate — zx fills the bandwidth between your storage and the cluster, and scales out with no software ceiling. Proven at 1 PB in 29 hours with SLAC and ESnet.
Yes. zx scales out — add servers and aggregate throughput grows, with no software ceiling. Your environment sets the limit, not the data mover, so the appliance grows with the cluster instead of becoming the next bottleneck.
Yes — file and object movement across on-premises, hybrid, and public cloud, bypassing the 30-50% cloud-stack penalty.
Yes. zx runs on NVIDIA BlueField, offloading the host CPU — roughly 4U versus about 13U for an equivalent x86 setup, per NVIDIA.
See the zx Appliance keep your AI infrastructure fed at wire speed.