NeuralMesh Axon:
Unlock The Full Potential of Your GPUs
Seamlessly fuse compute and storage to shatter AI performance barriers and radically reduce infrastructure and costs.
The world’s leading AI innovators and research teams build with WEKA
“Embedding WEKA’s NeuralMesh Axon into our GPU servers enabled us to maximize utilization and accelerate every step of our AI pipelines. The performance gains have been game-changing: Inference deployments that used to take five minutes can occur in 15 seconds, with 10 times faster checkpointing.”
Why NeuralMesh Axon?
Deploy NeuralMesh™ Axon™ directly on your GPU compute to get ultra-fast storage without adding separate infrastructure.
Accelerate Performance
Get unmatched performance and utilization for the largest AI training and inference workloads.
Drive Efficiency
Consolidate compute and storage to reduce rack space, power, and cooling. Cut costs and run leaner, smarter infrastructure at scale.
Break Memory Barriers
Leverage add‑on capabilities like Augmented Memory Grid to offload KV‑cache overflow and remove memory constraints.
Instant Availability
Container‑native microservices deliver large-scale readiness on day one and fuse compute and storage for on-premises and cloud environments.
Reduce AI Infrastructure
Run GPUs on‑prem or in the cloud without external storage infrastructure.
Power Demanding Workloads
Deliver ultra low-latency, high-throughput storage performance for the most demanding use cases across AI, media, finance, and healthcare.
See How They’re Deployed
View Deployment Architectures for NeuralMesh and NeuralMesh Axon
“With WEKA’s NeuralMesh Axon seamlessly integrated into CoreWeave’s AI cloud infrastructure, we’re bringing processing power directly to data, achieving microsecond latencies that reduce I/O wait time and deliver more than 30 GB/s read, 12 GB/s write, and 1 million IOPS to an individual GPU server.”
Capability Comparison for NeuralMesh and NeuralMesh Axon
| NeuralMesh | NeuralMesh Axon | |
|---|---|---|
| Capability | ||
| Physical Footprint | Moderate reduction | Significant reduction (including rack space, power, cooling, and networking) |
| Recommended GPU Server Nodes | No specific minimum | Typically recommended for 128+ GPU nodes |
| Tiering Support | Supported | Not recommended |
| Single Cluster Multi-Client Configuration | Supported | Not currently supported |
| Supported Protocols for Direct Data Access | POSIX, S3, NFS, SMB | POSIX only |
| Resource Management | Flexible | Typically managed via Kubernetes or SLURM |
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