Training Systems
End-to-end training infrastructure from optimizer math to fault tolerance at 10K GPUs.
Memory & Optimizer Internals
GPU Memory Anatomycoming soon
Where memory goes: parameters, gradients, optimizer states (Adam m/v), and activations.
Mixed Precision Trainingcoming soon
FP32, BF16, FP8, Transformer Engine — which tensors at which precision and why.
The Memory Wallcoming soon
Why a 70B model in BF16 needs ~140GB before gradients or optimizer states.
Activation Memory Analysiscoming soon
What is saved for backward, per-layer cost, and strategies to reduce it.
Memory Optimization Techniques
ZeRO Stage 1/2/3coming soon
Partitioning optimizer states, gradients, and parameters across data-parallel ranks.
FSDPcoming soon
PyTorch Fully Sharded Data Parallel — ZeRO-3 in native PyTorch.
Activation Checkpointingcoming soon
Recompute vs store tradeoff — trading compute for memory.
CPU & NVMe Offloadingcoming soon
ZeRO-Offload, ZeRO-Infinity — moving cold state off GPU, prefetching before use.
Distributed Launch Mechanicscoming soon
torchrun, RANK, LOCAL_RANK, WORLD_SIZE, elastic launch with fault tolerance.
Pipeline Scheduling
Micro-batching & Gradient Accumulationcoming soon
Processing multiple small batches before one parameter update.
Pipeline Schedulescoming soon
GPipe, 1F1B, interleaved 1F1B, zero-bubble schedules — tradeoffs and efficiency.
Communication Overlapcoming soon
Gradient bucketing, starting AllReduce before backward finishes.
MoE-Specific Schedulingcoming soon
Overlapping All-to-All with computation in mixture-of-experts training.
Pipeline Bubble Analysiscoming soon
Quantifying efficiency loss from pipeline bubbles and strategies to minimize them.
Fine-Tuning & Adapters
LoRAcoming soon
Low-rank adapters — where to apply, rank selection, and training efficiency.
QLoRAcoming soon
Quantized base model + FP16 adapters — fine-tuning large models on limited hardware.
SFT Pipelinescoming soon
Chat templates, sequence packing, curriculum design, evaluation checkpoints.
Multi-Adapter Servingcoming soon
S-LoRA unified paging, Punica batched GEMM, adapter hot-swapping on shared base models.
Continual Learningcoming soon
Catastrophic forgetting, replay buffers, elastic weight consolidation.
Adapter Registry & Versioningcoming soon
Managing adapter lifecycle, A/B testing different adapters in production.
RLHF & RL Infrastructure
RLHF/GRPO Pipelinecoming soon
Generate rollouts → score with reward model → compute policy gradients → update.
Rollout Orchestrationcoming soon
Dedicated inference instances for generation, variable-length batching strategies.
Reward Model Servingcoming soon
Co-hosting reward/verifier models alongside policy models, external tool environments.
Agent RL Infrastructurecoming soon
Sandbox management for RL agents, security isolation, output capture, cost limits.
Dynamic Resource Allocationcoming soon
Async vs sync policy updates, elastic allocation between rollout and training.
Fault Tolerance & Efficiency
Checkpointing Strategiescoming soon
Synchronous vs asynchronous, distributed checkpointing, optimal save interval.
Fault Detection & Recoverycoming soon
Heartbeat monitoring, automatic rank replacement, elastic training with fewer GPUs.
Cluster Efficiency Metricscoming soon
MFU, GPU utilization, bubble ratio, communication overhead — diagnosing the bottleneck.
Training Diagnosticscoming soon
Loss spikes, gradient anomalies, silent data corruption, thermal throttling.