Systems-layer GPU training for AI engineers

Train LLMs at production depth.

The only course that teaches NCCL tuning, GPUDirect RDMA, FSDP vs ZeRO-3 trade-offs and profiling-driven optimization from real production cluster work — not documentation rewrites.

4.2→0.8ms AllReduce latency
87% GPU utilization
7d→18h Training speedup
$480K Saved per year
You're on the list. I'll email you when the curriculum drops.

// No spam. Early access pricing for waitlist only.

AllReduce 4.2ms → 0.8ms 16-node A100 cluster GPU utilization 61% → 87% InfiniBand NDR 400Gbps 7 days → 18 hours training GPUDirect RDMA bypass $480K–$600K annual savings Utility patent filed FSDP / ZeRO-3 production depth Nsight Systems + Nsight Compute SHARP in-network aggregation 13B-parameter LLM fine-tuning AllReduce 4.2ms → 0.8ms 16-node A100 cluster GPU utilization 61% → 87% InfiniBand NDR 400Gbps 7 days → 18 hours training GPUDirect RDMA bypass $480K–$600K annual savings Utility patent filed FSDP / ZeRO-3 production depth Nsight Systems + Nsight Compute
The problem

Every course stops
where the hard part starts.

01 //

You know how to call PyTorch's distributed APIs. You don't know what's happening inside NCCL when your AllReduce stalls and costs you 8% step time.

02 //

You've read the FSDP docs. Nobody has explained the actual trade-offs between FSDP and ZeRO-3 at batch size 512 on a real A100 cluster.

03 //

You know Nsight Systems exists. You've never used it to root-cause a training bottleneck in under 30 minutes the way a production engineer does.

04 //

GPU compute is expensive. Nobody has taught you the systems-layer levers that cut cluster costs 40% without sacrificing throughput — it's a patented approach for a reason.

Who this is for

Built for engineers
already in the arena.

ML / Training engineers
You run distributed training jobs and know something's wrong with your GPU utilization. You want to fix it at the systems layer, not guess with config changes.
HPC infrastructure engineers
You manage GPU clusters and are expected to optimize throughput. You want the profiling methodology and NCCL internals knowledge to diagnose any bottleneck.
AI platform / MLOps engineers
You deploy and scale inference systems. You want to go deeper on TensorRT-LLM, PagedAttention, and the production serving stack that hits 95ms P50 at scale.

// NOT for beginners. This course assumes you write Python, understand gradients, and have at least touched a GPU workload.

Course curriculum

8 modules.
40+ lessons. Zero fluff.

01
GPU architecture & memory hierarchySM architecture, HBM2e, NVLink vs PCIe, A100 vs H100
Foundation
02
CUDA programming for ML engineersKernel writing, stream pipelining, kernel fusion, CUDA Graphs
Foundation
03
Distributed training frameworks — deep diveFSDP vs ZeRO-3 trade-offs, tensor/pipeline parallelism, MFU benchmarking
Core
04
NCCL, collective ops & network tuningAllReduce internals, NCCL environment tuning, InfiniBand, SHARP
Core
05
GPUDirect RDMA & high-speed networkingRDMA bypass, ReduceScatter+AllGather, RoCE vs InfiniBand
Advanced
06
Profiling-driven optimizationNsight Systems, Nsight Compute, VTune — from symptom to root cause
Advanced
07
Inference systems at scaleTriton Inference Server, TensorRT-LLM, PagedAttention, speculative decoding
Mastery
08
GPU cost optimization & cluster economicsAdaptive scheduling, mixed precision, the patented cost governance approach
Mastery
Your instructor
SS
Sankar Sathish
Panneer Selvam
AI HPC Infrastructure Architect

I'm not an academic. Everything in this course comes from 7 years of production GPU cluster engineering — diagnosing real bottlenecks, shipping real optimizations, and defending them with real numbers.

I reduced AllReduce latency from 4.2ms to 0.8ms on a 16-node A100 cluster. I pushed GPU utilization to 87% on a 13B-parameter LLM fine-tuning job. I filed a utility patent on adaptive GPU cost optimization. Every lesson in this course is built from that experience — not from reading documentation.

14 years in enterprise distributed systems
7 years specializing in GPU cluster engineering
Utility patent — adaptive GPU cost optimization
3 technical publications on distributed LLM training
25+ engineers mentored at Capgemini & Ericsson
$2M+ business value delivered from AI infrastructure
Early access pricing

Waitlist pricing.
Locked in forever.

Self-paced
$299 /early
Full course access. Go at your own speed. Lifetime access to all content and future updates.
  • All 8 modules + 40+ lessons
  • Lab exercises with solutions
  • Profiling tool templates
  • Private community access
  • Lifetime updates
MOST VALUE
Live cohort
$1,499 /seat
10-engineer cohort. Weekly live sessions with Sankar. Real cluster labs. Direct feedback on your workloads.
  • Everything in self-paced
  • Weekly live sessions (10 weeks)
  • Bring your own cluster workload
  • Direct Slack access to Sankar
  • Certificate of completion

// Waitlist price locks in. Launch price will be $499 / $1,999. Join now to save.

Stop guessing.
Start profiling.

Join engineers who want production depth, not framework tutorials.

You're on the list. Early access pricing is locked in for you.