DPU Use Cases
Powered by MPPA® architecture, Kalray’s Data Processing Unit technologies deliver performance, efficiency, and scalability where it matters most—enabling smarter AI, accelerated storage and networking, and more secure infrastructure from AI factories and HPC to cloud and edge.
Storage and Data
Data movement, encryption, and I/O processing are major bottlenecks in modern storage infrastructures. DPUs bring parallelism and dedicated processing to eliminate those bottlenecks—especially for AI, HPC, and cloud environments.
Common Use Cases
- High performance RDMA
- Data transfers between CPU, GPU, storage
- NVMe-oF, storage disaggregation and virtualization
- Data encryption and compression
WHY DPUs?
- Removes CPU/GPU load for storage functions
- Scales to high-throughput workloads
- Secure, deterministic packet and data processing
Networking
Packet processing and traffic flow management are placing increasing strain on CPU resources when using traditional Network Interface Cards. DPUs overcome these limitations by offloading and accelerating packet and flow processing directly on the DPU—freeing CPU cycles while delivering higher throughput, lower latency, and more efficient networking across AI and HPC fabrics, as well as cloud environments.
Common Use Cases
- Packet processing acceleration
- Cloud native networking
- High-performance virtual switching
- AI and HPC fabric optimization
WHY DPUs?
- Achieve deterministic, terabit throughputs without burning CPU cores
- Seamlessly offload virtual networking to dedicated hardware
- Reduce latency for AI and HPC fabrics
Security
Fine-grained security processing, encryption, and isolation are becoming essential in modern public and multi-tenant infrastructures. DPUs offload critical security functions - such as encryption, policy enforcement, segmentation, inspection, and threat detection - onto dedicated hardware, delivering strong isolation and enabling zero-trust security at wire speed.
Common Use Cases
- Inline encryption/decryption
- Hardware based policy enforcement
- Secure multi-tenant segmentation and isolation
- Infrastructure services protection from host workloads
WHY DPUs?
- Designed for inline, real-time security tasks
- Software-defined, adaptable policy engines
- Security with zero CPU tax, physical hardware separation
Artificial Intelligence
As AI workloads grow more distributed and LLM architectures get more complex, network and data movements are now a first-class performance bottleneck. DPUs are becoming a foundational building block for modern AI infrastructure by offloading critical tasks such as RDMA communication, storage-to-GPU data paths, KV cache transfers, keeping GPUs fed, utilized, and focused entirely on inference and training.
Common Use Cases
- AI data movements optimization
- AI fabrics acceleration
- Secure multi-tenant AI infrastructure
- KV Cache Transfer for Prefill / Decode Disaggregation
WHY DPUs?
- Increase AI infrastructure performance and efficiency by removing current bottlenecks
- Accelerate and optimize data transfers
- Enable scalable distributed AI inferencing
5G and Telco Edge Clouds
5G and telco edge clouds demand wire-speed packet processing, ultra-low latency, and support for dense, virtualized network functions. DPUs offload and accelerate critical workloads such as vRAN and UPF directly in silicon, freeing CPUs for higher-value tasks. They deliver cloud-native efficiency with predictable performance for the most demanding 5G and edge environments.
Common Use Cases
- vRAN / Open RAN acceleration
- UPF packet processing offload
- Edge security, encryption, and isolation
- Network slicing enforcement
WHY DPUs?
- Real-time, low-latency execution
- Deterministic packet scheduling
- Software-programmable updates for evolving 5G workloads
Custom DPU Use Cases
From aerospace to deep tech, not every workload fits a predefined category. DPUs can be custom-designed for virtually any compute-intensive or latency-sensitive task. Whether you’re building specialized pipelines, protocol stacks, or unique AI flows, DPUs can be architected to give you full control over performance, power, and flexibility.
Common Use Cases
- Proprietary protocol offload
- Specialized AI model inference
- Sensor fusion & data pre-processing
- Industry specific hardware acceleration
WHY DPUs?
- Architecture tailored to customer-defined workloads
- Flexible software-defined pipelines and deterministic scheduling
- Suitable for industries with specific needs: aerospace, defense, fintech