Product Roadmap

Our research-first approach to infrastructure optimization. From hardware-aware memory pooling to energy-efficient kernel tuning.

Released • Available Now

Optimemory & HyperRAG

CUDA VMM layer for single-node GPU hardware and KV cache optimization for RAG serving. Both packages are available on PyPI today.

  • deep-variance on PyPI
  • dv-hyperrag on PyPI
  • Physical memory pooling via CUDA VMM
  • Up to 5x faster TTFT on long-context RAG
Early Access • Beta

DeepTuner

Static PTX analysis for energy-efficient kernel configurations on HPC infrastructure. Jointly tunes thread block shape and GPU power cap for minimum energy per token, without runtime profiling.

  • Static PTX analysis (no runtime profiling)
  • Up to 79% energy savings per token
  • 93.4% reduction in kernel search space
  • DeepTuner dashboard (planned)
Research • Ongoing

Multi-GPU & NVLink Virtualization

Extending virtual address stitching across multiple GPUs via high-speed NVLink interconnects, presenting a unified global address space to the model runtime.

  • Cross-GPU VMM stitching
  • NVLink paging layer
  • Multi-node orchestration