The optimization layer
between framework
and silicon.
Deep Variance is the runtime optimization layer between PyTorch or vLLM and the CUDA driver. Same call graph in, optimized work out, no model changes.
One request, end to end.
The intercept sits below the framework and above the driver. Your app keeps the same API. The GPU gets a cleaner path to silicon.
Application
Your model code issues a forward, generate, or training step.
Framework
PyTorch, vLLM, SGLang, or TensorRT-LLM dispatches the call.
- Intercept layer
Deep Variance intercept
Memory, KV cache, and kernel calls are rewritten in place. Semantics preserved.
CUDA dispatch
Rewritten calls reach the driver with the original tensor shapes and dtypes.
GPU
Execution runs on recovered VRAM, warm caches, and tuned kernels.
Three modules.
One install.
Each attaches at a different layer. Run one, two, or all three.
- Live
Optimemory
Run a bigger model or a bigger cache on the GPUs you already have.
Read the full Optimemory page+65%more usable VRAM per card - Beta
HyperRAG
When the same RAG context comes back, serve it from cache instead of recomputing prefill.
Read the full HyperRAG page6xfaster first token on cache hits - Early
DeepTuner
Keep the same QPS while cutting energy per token. No new hardware required.
Read the full DeepTuner page−50%lower energy per token
What changes.
What stays.
The layer is non-invasive. Training code, model weights, and orchestration are untouched.
What changes
- How VRAM is allocated and reclaimed
- How KV cache is scheduled and reused
- Which kernel config is chosen per shape
- Headroom for larger batches and longer context
What stays
- Your models and weights
- Your training and serving pipelines
- Your framework version and Python API
- Your containers, schedulers, and CI
See the numbers
on your workload.
Send a representative job. Get a baseline-vs-Deep-Variance report back within two weeks.