Most AI teams are losing
$300K–$600K/year
in GPU compute.
Not from hardware failures. From silent misconfigurations — NCCL buffer misalignment, MTU boundary fragmentation, disabled in-network compute, and parallelism strategies that destroy scaling efficiency. These don't trigger alerts. They just make training slow and expensive.
Fixed price. 5-day delivery. Identifies $300K+ of wasted GPU capacity or I'll tell you why not.
Surgical infrastructure diagnosis.
Not generic consulting.
I go into the cluster, run real benchmarks, and find exactly which layer is destroying your compute efficiency. Most engagements find the primary waste source within 48 hours.
MFU measurement, kernel utilization, wave quantization effects, XLA/CUDA compilation overhead. Most clusters run at 15–35% MFU. The theoretical ceiling is 65%+.
Benchmark: MFU 7% → 40%AllReduce algorithm selection, buffer size alignment to MTU, SHARP enablement, ring vs tree topology, InfiniBand vs RoCE tuning. One misconfiguration = 30% step time overhead.
4.2ms → 0.8ms AllReduceActivation checkpointing overhead, gradient accumulation strategy, KV cache fragmentation, input pipeline stall time. Memory inefficiency silently adds 10–25% to step time.
Step-time breakdown per layerDP vs TP vs PP trade-offs for your model size, what breaks going from 8→128→1024 GPUs, gradient synchronization overhead at scale, near-linear scaling diagnosis.
Scaling efficiency formulaThis is not for everyone.
It's built for teams with real clusters.
- Running 8–1024+ GPU clusters for LLM training or fine-tuning
- Training time feels longer than it should — but no clear root cause
- GPU utilization metrics look "fine" but costs are climbing
- Scaling from 8→128 GPUs and efficiency is dropping
- NCCL is configured but never been benchmarked
- CTO or AI infra lead who needs data, not theory
- Gulf, US, or UK team with serious compute budget ($25K+/month)
- Fewer than 8 GPUs — diagnostic ROI won't justify the cost
- Running pure inference only (no training workloads)
- Looking for general ML consulting or model architecture advice
- Need help with MLOps pipelines, CI/CD, or data engineering
- Early-stage with no production training runs yet
- Expecting a presentation deck, not a working benchmark report
If your cluster runs at below 40% MFU, you are already losing money on every training step. The industry average for untuned clusters is 15–35% MFU. You likely qualify.
Not case studies from documentation.
From production clusters.
128-GPU A100 cluster. Three NCCL changes. $480K annual savings. Zero new hardware purchased.
16-node A100 cluster. Ring algorithm on fat-tree, buffer misalignment, SHARP disabled. One afternoon to fix.
Real GCP multi-node benchmarks. MTU cliff at 64KB — 21μs → 158μs. The default state of most clusters.
If your cluster is below 40% MFU,
you're already losing money.
A $5K diagnostic pays for itself the moment we find the first configuration fix — which typically happens within 48 hours of starting. The question isn't whether you have inefficiency. It's how much.
Fixed price: $5,000 · 5-day delivery · Written report + walkthrough call