// GPU Cluster Diagnostic · $5,000 · 5-day delivery

Find where your cluster
is losing $300K+/year.

Most AI teams assume slow training is a hardware limitation. It isn't. 30–60% of GPU compute is lost to silent misconfigurations — NCCL buffer misalignment, MTU boundary fragmentation, parallelism strategy mismatch, and GPU idle time that never appears in utilization dashboards.

Industry average MFU for untuned clusters: 15–35%. Theoretical ceiling: 65%+. The gap is recoverable compute — and it has a dollar value. Backed by real 128-GPU A100 cluster diagnostics.
→ Check Your Cluster (20-min diagnostic call)

No commitment. If nothing is wrong, I'll tell you in 48h.

⚡ Only 2 diagnostic slots available this month.

Focused only on GPU cluster performance and distributed training systems.

$5,000
Fixed price
5 days
Delivery
$300K+
Typical waste identified
7%→40%
MFU delivered
$480K
Savings on single cluster
GPU clusters
only
Specialist. Not generalist.
// Not for everyone
This diagnostic is not for teams running <8 GPUs, teams not training models regularly, or teams looking for generic consulting or MLOps advice.
// Built for
Teams running 8–1024+ GPU clusters for LLM training or fine-tuning where compute costs are real and step time is slow. If you're running real workloads at scale, this will find something.
// What You Get

Five deliverables. Every engagement.

Not a generic report. A precise breakdown of your cluster's inefficiency — layer by layer, with specific fixes and the dollar value of each.

01
MFU Baseline Measurement

Achieved FLOPs vs theoretical FLOPs across your training workload. Normalized for architecture-specific XLA/CUDA compilation and interconnect behavior. You'll know your exact efficiency floor.

Output: MFU % with benchmark comparison
02
Step-Time Breakdown

Separation of compute time, AllReduce/communication time, input pipeline time, and XLA compile time per training step. Most teams have never seen this breakdown — it shows exactly where time goes.

Output: Per-step time attribution chart
03
NCCL Configuration Audit

AllReduce algorithm selection, buffer size alignment to MTU, SHARP enablement, ring vs tree topology analysis, IB vs RoCE tuning. NCCL misconfiguration is the #1 source of recoverable waste.

Output: NCCL tuning configuration file
04
Scaling Efficiency Diagnosis

What breaks going from 8→128 GPUs. Parallelism strategy analysis (DP vs TP vs PP), gradient synchronization overhead at scale, near-linear scaling gap identification.

Output: Scaling efficiency formula
05
Root Cause Report + Walkthrough

Written report with ranked findings, dollar value per finding, and specific configuration fixes. Followed by a 60-minute walkthrough call where every finding is explained and questions answered.

Output: PDF report + 60-min call
+
GitHub Benchmark Data

All benchmark methodology, scripts, and raw data shared. You own the data. Reproducible by your team. Not a black-box report.

Output: Benchmark repo access
// Sample Findings — From Real Clusters

What gets found. Every time.

These aren't hypothetical. They're the patterns that appear in every untuned cluster — and they have known fixes.

NCCL · Communication Layer
MTU Boundary Latency Cliff

NCCL buffer size misaligned to fabric MTU. Messages fragment at the boundary — every AllReduce chunk crosses a 7× latency cliff. Invisible in training logs. Shows up as step-time variance.

Typical cost: $120K–$180K/year on 128-GPU A100 cluster
NCCL · Algorithm Selection
Ring AllReduce on Fat-Tree Topology

NCCL defaulting to ring algorithm on a fabric designed for tree topology. Communication pattern fights the physical network — 30–50% AllReduce overhead. One environment variable to fix.

Typical cost: $80K–$150K/year in wasted step time
Compute · GPU Utilization
GPU Idle Time During AllReduce

SHARP disabled — in-network compute sitting idle while CPU handles reductions it was never designed for. GPU waits. Utilization dashboard shows 85%+ but actual compute MFU is 15–25%.

Typical cost: $100K–$200K/year in idle GPU time
Memory · I/O Layer
Activation Checkpointing Overhead

Checkpointing strategy recomputing more than necessary — adding 15–25% to step time with no corresponding memory saving. Gradient accumulation misconfigured for the actual batch size.

Typical cost: $60K–$120K/year in excess recomputation
// ROI — The Math

The diagnostic pays for itself
before the report is delivered.

30–60%
Typical GPU compute waste in untuned production clusters
$100K–$600K
Annual dollar loss on clusters running $25K–$100K/month
48h
First bottleneck identified in most engagements — before the full report is delivered
Example: 128-GPU A100 Cluster
Monthly compute cost$25,000
Waste from NCCL misconfiguration (20% of step time)$5,000/month
Annual waste$60,000/year
Waste from MFU gap (7% → 40% = 33% recoverable)$99,000/year
Diagnostic cost$5,000
Recoverable annual value$159,000–$480,000
That's the difference between scaling your models — or burning budget.
Your cluster is costing you right now.
First bottleneck identified within 48h. · ⚡ 2 slots left this month.
→ Check Your Cluster
// How It Works

Four steps. Five days.

No long onboarding. No retainer. A focused engagement with a clear output.

1
20-Min Discovery Call

Cluster size, training workload, current utilization metrics, what "slow" looks like. I confirm the diagnostic will find something worth fixing — or I'll tell you why not.

Day 0
2
Data Sharing

Cluster config, NCCL environment variables, nsys/DCGM profiles, nccl-tests output. Secure share — no production access required. I work from the data, not your systems.

Day 1
3
5-Day Analysis

Full diagnostic run — MFU measurement, step-time breakdown, NCCL audit, scaling analysis. Root cause identified and ranked by dollar impact.

Days 1–5
4
Report + Walkthrough

Written PDF report with ranked findings, dollar value per finding, specific configuration fixes, and a 60-minute walkthrough call. You leave knowing exactly what to fix and in what order.

Day 5
// Pricing

One price. No surprises.

$5,000
fixed / engagement

A single fixed-price engagement. No retainer, no scope creep, no hourly billing. You know the cost before we start. The diagnostic either finds value or it doesn't — and I'll tell you upfront if I think your cluster won't qualify.

  • MFU baseline measurement across your training workload
  • Complete step-time breakdown — compute / comm / I/O / compile
  • Full NCCL configuration audit with tuning config file
  • Scaling efficiency diagnosis and parallelism analysis
  • Written PDF report with ranked findings and dollar values
  • 60-minute walkthrough call with Q&A
  • Benchmark scripts and raw data — you own everything
  • Follow-up support for 30 days on implementing the fixes
Typical finding: $300K–$480K of recoverable annual GPU compute. The diagnostic pays for itself 60–96× over.
Risk reversal: If your cluster is already fully optimized, I'll tell you in the first 48 hours — before the full engagement runs. You won't pay for a report that says everything is fine.

⚡ Only 2 diagnostic slots available this month.

→ Check Your Cluster (20-min call) sankar@nydux.ai

No commitment required on first call. First insight in 48h.

// FAQ

Common questions.

Do you need access to our production cluster?
No. I work from profiling data you share — nsys profiles, DCGM metrics, NCCL environment variables, nccl-tests output. No production access required. Everything stays on your systems.
What cluster size is minimum?
8 GPUs minimum for the diagnostic ROI to make sense. The diagnostic is designed for teams running 8–1024+ GPU clusters for LLM training or fine-tuning. Smaller clusters are better served by a free 20-minute consultation.
What if you don't find anything significant?
I'll tell you on the discovery call if your cluster is unlikely to have significant recoverable waste. I won't take $5,000 to tell you your cluster is already well-tuned. In 14 years, I haven't encountered an untuned cluster with nothing to find — but the call is how we verify.
What frameworks and hardware do you support?
PyTorch DDP, FSDP, DeepSpeed, TensorFlow Distributed, JAX/XLA. Hardware: NVIDIA A100, H100, H200. Interconnect: InfiniBand HDR/NDR, RoCE v2, 400G/800G Ethernet. Cloud: GCP, AWS, Azure, on-premise.
Can this lead to an ongoing engagement?
Yes. Many diagnostics convert to Fractional AI Infrastructure CTO retainers ($8K–$15K/month) where I continue working with your team on scaling strategy, training run optimization, and cluster expansion planning. But there's no pressure — the diagnostic stands alone.

Your cluster is wasting compute
right now. Find out how much.

A 20-minute call determines whether the diagnostic makes sense for your cluster. No commitment required.

⚡ Only 2 diagnostic slots available this month. First insight in 48h.
→ Start Diagnostic (20-min intro)

If your cluster is already optimized, I'll tell you in 48h — before the full engagement runs.

No production access needed Gulf · US · UK Response in 24h 128-GPU track record