Deep Variance provides a runtime layer that maximizes GPU utilization for AI inference by reclaiming wasted memory, eliminating redundant work, and optimizing chip usage.
Funding
Funding not disclosed
Founders
Product
Problem
AI inference workloads often leave significant GPU capacity unused because existing stacks waste VRAM, repeat computation, and lack fine‑grained kernel tuning. This inefficiency forces organizations to provision larger GPU fleets, increasing power consumption and infrastructure costs while limiting model size and request throughput.
Solution
Deep Variance introduces a runtime optimization layer that sits between popular frameworks (e.g., PyTorch, vLLM, SGLang, TensorRT‑LLM) and the CUDA driver. The layer intercepts memory allocations, KV‑cache scheduling, and kernel launches, reclaiming unused VRAM and eliminating redundant work without altering model code, weights, or container orchestration. By dynamically selecting tuned kernel configurations for each tensor shape, it accelerates inference up to six times and expands usable memory by roughly 65 %. The same API and scheduler remain intact, allowing a simple install‑once deployment that immediately reduces power draw per token and lowers overall GPU fleet requirements while preserving SLA performance.
Target Audience
Primary customers are organizations that run large‑scale AI inference services—such as cloud providers, enterprise AI platforms, and LLM deployment teams—that need higher GPU utilization without rewriting their existing pipelines.
Features
- In‑place VRAM reclamation and KV‑cache reuse that adds up to 65 % more usable memory per GPU
- Automatic elimination of repeat work in serving pipelines, boosting throughput
- Real‑time kernel configuration tuning per tensor shape for up to 6× faster inference
- Drop‑in integration with existing frameworks (PyTorch, vLLM, SGLang, TensorRT‑LLM) and no code changes required
- Compatibility with current container, scheduler, and CI setups; only the runtime layer is added
- Energy‑efficient execution that cuts power draw per token by approximately 50 %
- Supports NVIDIA GPUs in production, including H100 and other current silicon