Advanced AI Stacks
ML and AI Workshops
Accelerating Intelligence: From Model Architecture to Large-Scale Distributed Training.
Scaling AI Beyond the Desktop
The transition from local development to HPC-scale AI requires a fundamental shift in how we handle data parallelization and hardware utilization. Our workshops target the intersection of Data Science and Systems Engineering.
Core Workshop Modules:
- Distributed Training Deep-Dive: Implementing Data Parallel (DP), Distributed Data Parallel (DDP), and Fully Sharded Data Parallel (FSDP).
- GPU Memory Optimization: Techniques for Gradient Checkpointing, Mixed Precision (FP16/BF16), and Quantization (INT8/NF4).
- AI Orchestration: Deploying AI workloads via Kubernetes (K8s) and Slurm, using NVIDIA Enroot or Apptainer for containerized performance.
Framework Focus:
We specialize in production-ready AI frameworks and hardware-specific optimizations.
PyTorch Distributed
TensorFlow XLA
NVIDIA Triton
Hugging Face Accelerate
DeepSpeed
Post-GPU Readiness:
Special modules for Groq (LPU) and Cerebras (CS-3) integration are available upon request.
Workshop Methodology: Training -> Scalability
| Domain | Target Action | Outcome |
|---|---|---|
| Model Scaling | Moving from single-GPU training to multi-node distributed setups. | Reduced training time (from days to hours). |
| Inference Optimization | Model pruning, quantization, and deployment on Triton Inference Server. | Ultra-low latency for production-grade AI agents. |
| Data Pipelines | Orchestrating Zero-Copy data flows from NVMe storage to GPU memory. | Elimination of I/O wait-states in AI training loops. |
| LLM Operations | Fine-tuning strategies (LoRA/QLoRA) and RAG architecture implementation. | Context-aware, private enterprise AI solutions. |