Production C++/CUDA Interposition Engine

Eliminate GPU Power Spikes.
Unlock Stalled Megawatts.

VoltGrid AI introduces nanosecond-precision rank phase staggering into collective communication—flattening synchronized power draw across data center racks without adding model latency.

Request 2-Week Pilot
LD_PRELOAD libnccl-voltflow.so
85.1%
Peak dI/dt Current Surge Mitigation
< 0.2%
Training Step Latency Impact
0 Lines
PyTorch Code Modification Required
+20–30%
Safely Recoverable Rack Power Capacity

Hardware Power Benchmark Comparison

CALIBRATION: 4x GPU SHARED BUS • TAU = 25μs VRM FILTER
VoltGrid AI Before and After Control Benchmark
MULTI-LAYER TRANSFORMER BENCHMARK • SELECTIVE FILTERING VERIFICATION
VoltGrid 2.0 Transformer Workload Benchmark

How VoltGrid 2.0 Operates

01 / INTERPOSITION

Sub-100ns C++ Interceptor

Sits dynamically at the NCCL transport boundary via LD_PRELOAD. Injects hardware CUDA stream barriers without recompiling model code.

02 / SELECTIVITY

Selective Collective Filtering

Inspects buffer sizes to bypass fine-grained layer activations (TP) below 5MB—completely eliminating the latency penalty of naive staggering.

03 / ADAPTABILITY

Residual Jitter Compensation

Measures arrival timing variations and injects only the residual microsecond delay, preventing tail-latency straggler cascade across the cluster.

Ready to Test on Your Test Rack?

We deploy a pre-compiled, closed-source libnccl-voltflow.so binary on a 32–64 GPU test cluster for a free 2-week validation trial.

Schedule a 10-Minute Call