AI Inference
Engineered for Speed & Scale
Do more with less. Serve larger models, heavier traffic, and longer contexts on high-efficiency AMD Instinct™ infrastructure built for scale.
Optimized Cloud for High‑Performance Inference
Built for Modern AI
Infrastructure built for AI with more memory, longer contexts, higher throughput, and minimal latency
Kubernetes at Scale
Deploy production workloads to thousands of GPU workers with our high-performance Managed Kubernetes solution
Cluster-Scale Inference
Run disaggregated or multi-node inference across fully interconnected clusters powered by RoCEv2 networking
Compliant with SOC2 Type II, ISO 27001 & HIPAA standards to keep your data protected.
Trust CenterWhy it's faster with TensorWave
Designed for massive scale, optimized for every millisecond.
Lightning Fast Storage
Load model weights in record time and minimize replica spinup with our petabyte-scale flash-memory storage.
High Speed Networking
Handle distributed inference faster than ever with 400 Gbps networking and RoCEv2 interconnects.
Ultra-Wide GPU Bandwidth
Run disaggregated or multi-node inference across fully interconnected clusters powered by RoCEv2 networking.
Choose Your Model
Work seamlessly with your choice of LLM, based on the capabilities you need.
TensorWave's end-to-end Ethernet design streamlines performance for training and inference, scales easily as you grow, and cuts complexity
Powerful & Efficient
“We believe that the MI355X could be competitive against the HGX B200 for small to medium LLM production inference workloads. This is because the MI355X total cost of ownership is 33% lower than that of the HGX B200 for self-owned clusters, while it delivers much more HBM memory capacity, slightly more FP8 and FP4 TFLOP/s and double the FP6 TFLOP/s”
Accelerates Generative Video
“
TensorWave's AMD GPU cloud helped us increase efficiency by over 25% while reducing costs by up to 40%.
Alex Mashrabov, CEO, Higgsfield AI
Accelerates Generative Video
“
TensorWave's AMD GPU cloud helped us increase efficiency by over 25% while reducing costs by up to 40%.
Alex Mashrabov, CEO, Higgsfield AI
*Example frame: Higgsfield AI — generated video
Sized for your workload
Partition each GPU into 1, 2, 4, or 8 logical devices for unmatched flexibility.
Maximize utilization and throughput with smaller models like Llama 8B with parallel instances.