DevCert
We eliminate the physical bottlenecks of neural network training through deep-metal audits, thermal re-engineering, and memory bandwidth synchronization.
Every configuration is validated against real-world training weights, eliminating hypervisor noise for bare-metal performance.
We diagnose unexplained throttling in multi-node clusters by analyzing the heat transfer efficiency of the entire rack. This includes physical sensor calibration and liquid-flow optimization for high-density H100/A100 arrays.
Critical for LLM training where memory access speeds are the primary execution bottleneck. Tuning timings and clock frequencies for HBM3/GDDR6X to maximize token throughput per watt.
Optimization of the switching fabric for distributed training. We eliminate frame-wait latency in InfiniBand and NVLink meshes, ensuring your cluster functions as a singular computational unit rather than a collection of nodes.
Optimization is not a software switch. It is a structured 5-day cycle of thermal stress tests, bios-level profiling, and physical calibration to reach a scientific performance floor.
"We establish performance ceilings under maximum thermal load before a single line of training script is executed."
Establishment of baselines. We map current hardware inventory, power logs, and hardware-level instruction sets. This Phase 01: Baselining establish current performance ceilings under max thermal load.
Execution of the DevCert Threshold: a proprietary 48-hour stress test protocol for node stability. We force the hardware into peak-utilization states to identify thermal drift and voltage fluctuations.
Physical implementation of optimization markers. This includes re-padding heat sinks, interconnect re-routing, and finalized kernel-level parameter tuning for specific neural network architectures.
Initialize Project AuditFundamental infrastructure decisions define your lab's Total Cost of Ownership (TCO). While air cooling offers simplicity and low maintenance overhead, liquid cooling is mandatory for modern high-density H100 arrays where air-flow resistance becomes a structural limitation.
Choose for edge nodes and distributed clusters where physical maintenance is infrequent.
Required for dense racks exceeding 50kW power draw and localized multi-GPU hubs.
Often, optimization focuses on maximizing the performance ceiling of your current silicon through thermal management and interconnect tuning before any hardware replacement is recommended.
Synthetic scores rarely reflect multi-hour training stability. We measure tokens per second per watt and training-step duration consistency to ensure your model converges without hardware-induced artifacts.
Our scope covers physical and BIOS-layer configuration. We do not support liquid-nitrogen cooling or experimental consumer-grade overclocking that compromises node reliability for ephemeral high scores.
Contact our infrastructure team to schedule an initial audit of your neural network clusters and eliminate thermal throttling at 1000 Rue Sherbrooke O, Montréal.