GPU cluster showing wasted energy from unoptimized kernel configurations

DeepTuner · Early access · HPC

Stop wasting energy
on unoptimized kernels.

DeepTuner predicts the most energy-efficient GPU kernel configuration before any code runs. Get up to 50% energy savings and 2x throughput without runtime profiling overhead.

Energy saved

Up to 50%

Throughput

Up to 2x

Works alongside

The problem

Runtime profiling is expensive.

Traditional kernel tuners require exhaustive runtime profiling across all possible configurations. For production clusters running continuous training, this profiling overhead is paid on every hardware migration and workload change.

50% energy waste: Unoptimized kernel configs burn energy on unnecessary compute

O(2ⁿ) search space: Hours of profiling for n tuning parameters on every hardware change

Compounding cost: Scales poorly with cluster size and model updates

50%

Energy waste from configs

O(2ⁿ)

Search space complexity

Every

Migration needs re-profiling

Scales

Poorly with cluster size

Static code analysis workflow producing optimal GPU configurations

How it works

Static analysis predicts optimal configs.

DeepTuner analyzes your GPU kernel code before any execution, extracting memory access patterns, control flow, and instruction mix to predict the most energy-efficient configuration and power cap settings.

No exhaustive runtime profiling. No O(2ⁿ) benchmark sweep. A one-time microbenchmark per GPU generation is all it needs to accurately predict optimal settings for any kernel.

Validated across H100, A100, RTX 5000 Ada, and RTX 3070 on multi-head attention, convolution, and matrix multiplication kernels.

What you get

Energy savings without sacrificing speed.

Up to 50% lower energy consumption per token across training and inference workloads. Your clusters run longer on the same power budget, reducing operational costs.

2x throughput gains on multi-head attention kernels. Same hardware, same model, twice the tokens per joule with optimized block shapes and power caps.

Zero profiling overhead. Migrate hardware generations or scale clusters without re-running expensive benchmark sweeps. Deploy with confidence on day one.

Optimized GPU showing reduced energy, increased throughput, and efficient memory access

Integration

Drop-in for CUDA and Triton.

DeepTuner integrates with your existing kernel development workflow. Analyze kernels, get optimized configs, and deploy with minimal code changes.

Works seamlessly with both CUDA and Triton kernels through static analysis of intermediate representations, requiring no modifications to your existing codebase.

CUDA kernels

Analyzes NVPTX intermediate representation. Works with hand-written CUDA or generated code.

Triton kernels

Extracts from LLVM IR before JIT. Predicts optimal configs per (kernel, GPU) pair.

On the horizon

Expanding beyond NVIDIA.

DeepTuner currently runs on NVIDIA GPUs. Work is actively in progress to bring the same intermediate code analysis approach to other hardware targets.

The core architecture is hardware-agnostic by design, making it possible to extend support to AMD ROCm, Google TPUs, and other accelerators with similar static analysis techniques.