I work with Series A–C AI companies and enterprise AI teams as a dedicated Fractional AI Infrastructure CTO — 20 hours/month, systems-layer depth, production results. No overhead, no equity, no 6-month ramp-up.
Most AI teams don't have a GPU problem. They have a coordination problem between compute, memory, and communication.
Until you measure those layers precisely — with Nsight Systems, nccl-tests, and kernel-level profiling — adding more GPUs only increases cost, not performance. The ceiling isn't hardware. It's visibility.
This is why I profile before I recommend. Every engagement starts with measurement, not assumptions.
This is for teams where GPU spend is growing faster than model performance —
and internal engineering cannot explain why.
Full profiling of your training and inference stack — Nsight Systems, Nsight Compute, nccl-tests. I find the bottleneck, fix it, and document it so your team can maintain it. Typical result: 20–40% throughput improvement in the first engagement.
FSDP vs ZeRO-3 trade-offs, tensor/pipeline/sequence parallelism selection, communication overlap strategy. I've scaled 13B-parameter LLM fine-tuning from 7 days to 18 hours. I'll do the same for your workload.
TensorRT-LLM, Triton Inference Server, PagedAttention, speculative decoding. I've deployed production inference at 95ms P50, 99.97% uptime. I'll design and validate your serving stack.
Adaptive spot/reserved scheduling, mixed precision strategy, cluster cost modeling. Based on a filed utility patent — I've delivered $480K–$600K annual savings on a single cluster. I'll build the same framework for yours.
Typical clients recover $200K–$600K/year in GPU efficiency gains. The engagement pays for itself within 30 days.
One 20-minute conversation to see if there's a fit. No commitment.