Hardware-aware infrastructure
for the AI stack.
Building hardware-aware optimization layers for the next generation of AI training stacks.
Four SDKs. One stack.
Each product targets a distinct bottleneck in the AI infrastructure pipeline.
Autopilot
End-to-end AutoML pipeline. Raw data to trained model in one call, powered by LLM-driven code generation and intelligent model comparison.
deepvariance-sdk Learn more Optimemory
Hardware-aware GPU VMM layer. Physical memory pooling and virtual address stitching for zero-overhead buffer reuse across training steps.
deep-variance Learn more LLM Tuner
FP8 weight quantization and fine-tuning tooling for large language models. Near-zero perplexity loss with significant memory savings.
dv-deeptuner Learn more HyperRAG
KV cache optimization for RAG serving. Prefix-trie caching, PGDSF eviction, and Pareto schedule search for up to 9x faster TTFT.
dv-hyperrag Learn more Closing the decade-long research gap
The best AI infrastructure algorithms are published years, sometimes decades, before industry ships them. Not from lack of effort, but because academic and production engineering require fundamentally different expertise that rarely coexists.
Why the gap persists
Academic research optimizes for correctness and novelty. Industry demands reliability, operational simplicity, and performance under real-world constraints. Bridging the two requires a team that speaks both languages fluently.
How we close it
We sit permanently at the intersection, tracking research as it is published and validating it against production workloads. Every SDK we ship is one less decade between a breakthrough and the teams who need it.
Who we build for
Four infrastructure problems we have studied in depth, with teams actively working through them.
+38%
fleet utilisation gain
Tenants over-provision to avoid OOM failures. Optimemory closes the gap at the driver level, turning stranded VRAM into a competitive advantage.
11w → 3d
pipeline build cycle
Regulated teams rebuild the same pipeline project after project. Autopilot automates it without transmitting a single raw record to an external service.
3B → 6B
model scale on same hardware
Labs hit VRAM ceilings before their science can scale. Optimemory recovers addressable memory at the driver level without touching training code.
50%
less VRAM for edge vision models
Inference must run on the factory floor, not the cloud. The full Deep Variance stack runs on-premise, air-gapped if required, with no data leaving the facility.
From the lab.
Research notes and engineering deep-dives from the Deep Variance team.
How VMM Stitching Recovers 65% of Wasted GPU Memory
A technical walkthrough of how Optimemory uses CUDA Virtual Memory Management to stitch fragmented VRAM into contiguous address spaces, eliminating allocation overhead.
FP8 Training: Achieving Near-Zero Perplexity Loss at Half the Memory
Our research into dual-format FP8 precision reveals that E4M3 forward passes combined with E5M2 backward passes maintain 99.9% accuracy while cutting memory in half.
Introducing HyperRAG: KV Cache Optimization for RAG Serving
HyperRAG combines prefix-trie KV caching, PGDSF eviction, speculative pipelining, and Pareto schedule search to deliver up to 2x faster time-to-first-token for RAG workloads.
Talk to the founders
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