AI Infrastructure

Step-by-step setup for vLLM’s disaggregated serving on NVIDIA GB200: separate prefill and decode workers, expert parallelism, FP4 quantization, and monitoring.

Build a GPU benchmarking toolkit that measures memory bandwidth, compute throughput, and training speed across precisions.

Speed up ML model deployments with a two-tier cache that pulls from S3 and falls back to local disk storage.

Distribute ML model files globally with CloudFront caching, signed URLs, and automated S3 uploads with boto3

Stop paying to store abandoned checkpoints and failed experiments by building an automated artifact GC pipeline on S3.

Push and pull ML model files through container registries with ORAS for versioned, cached distribution

Ensure model integrity and provenance by cryptographically signing and verifying model files before deployment

Combine MLflow for experiment logging with DuckDB for SQL analytics to find your best model configurations fast

Cut LLM inference costs by caching semantically similar requests with Redis and locality-sensitive hashing

Instrument your model serving layer to record token counts, compute costs per request, and alert when spending spikes

Process ML inference requests asynchronously with Celery workers and Redis, handling GPU batching and priority queues

Run a production-grade MLflow model registry with PostgreSQL storage, model versioning, stage transitions, and artifact management.

Ship a lightweight model registry on AWS that tracks versions, manages stages, and serves production models without MLflow overhead.

Autoscale ML model endpoints on Kubernetes using custom Prometheus metrics for inference latency and request queue depth

Set up a production-grade model serving cluster using Ray Serve with Docker containers and autoscaling replicas

Monitor exactly how much each model endpoint costs with custom Prometheus counters and Grafana panels

Route and load-balance ML inference traffic across model replicas with Envoy and gRPC

Serve multiple ML models with automatic routing, load balancing, and TLS using Docker Compose and Traefik.

Save hours of training time by implementing checkpoints that handle crashes, disk limits, and multi-GPU setups

Build a cost calculator that estimates ML training expenses before you spin up expensive GPU instances

Combine TensorBoard for ML metrics and Prometheus for system metrics into one training monitoring stack you can run locally.

Train and deploy ML models on SageMaker using the Python SDK with managed infrastructure and spot instances

Train HuggingFace models across multiple GPUs using Composer’s FSDP integration, callbacks, and built-in speed-up recipes.

Use Lightning Fabric to add multi-GPU and mixed precision training to your PyTorch code with minimal changes

Create a training job queue that manages GPU resources with Redis, worker pools, and job prioritization

Create a training job scheduler that checks GPU availability, queues jobs by priority, and exposes a REST API for submission

Scale PyTorch training across multiple nodes using Lightning Fabric with NCCL backend communication

Save thousands on GPU training by using spot instances with automatic checkpointing and multi-cloud fallback

Use torch.compile to make your PyTorch models faster with one line of code and understand when it helps most

Find and fix the bottlenecks slowing down your model training by monitoring GPU compute, memory, and data pipeline throughput

Cut your ML serving images from 8GB to under 2GB with multi-stage builds, slim base images, and smart caching

Serve more concurrent LLM requests on the same GPU by tuning KV cache memory, enabling PagedAttention, and using prefix caching

Set up NVIDIA Triton to serve multiple model formats with dynamic batching, ensembles, and real-time metrics for high-throughput inference.

Master GPU cluster setup with Slurm to run distributed training jobs, manage queues, and scale PyTorch across multiple nodes efficiently.

Train models faster and use less GPU memory by switching to mixed precision with just a few lines of PyTorch code

Ship your models to edge hardware fast with a proven ONNX-to-TensorRT pipeline that actually works

Convert your models to ONNX format and run them faster on CPU or GPU with fewer dependencies

Find and fix GPU memory bottlenecks so you can train larger models on the hardware you already have

Run 70B models on a single GPU by quantizing with AutoGPTQ and AutoAWQ in Python

Set up local LLM inference in minutes with Ollama or build llama.cpp from source for full control over quantization and GPU layers

Go from single-node training to distributed multi-GPU pipelines with Ray’s unified ML framework

Train 7B+ parameter models across multiple GPUs using DeepSpeed ZeRO stages, mixed precision, and CPU offloading.

Go from single-GPU to multi-GPU training with PyTorch DDP in under 50 lines of code

Cut your LLM latency in half by letting a small draft model propose tokens that the big model verifies in parallel.

Replace hand-rolled attention kernels with FlexAttention and get up to 2x faster LLM decoding on long contexts.