AI Platform & LLM Ops Mastery

Describe the autoregressive token-by-token generation process in LLM inference.

Identify the role of the attention mechanism and KV cache during inference.

Calculate model memory requirements based on parameter count and numerical precision.

Select the appropriate model serialization format (e.g., SafeTensors, GGUF) for a given deployment scenario.

Interpret a GPU specification sheet to identify key components for inference: CUDA cores, Tensor cores, and HBM.

Explain the GPU memory hierarchy and its impact on bandwidth bottlenecks in LLM workloads.

Relate the prefill (compute-bound) and decode (memory-bound) phases of inference to GPU resource utilization.

Monitor GPU utilization, memory usage, and thermal state using `nvidia-smi`.

Calculate KV cache memory consumption for a given model, sequence length, and batch size.

Analyze the impact of request batching on GPU throughput and latency using a benchmark script.

Measure the performance difference between static batching and continuous batching on an inference workload.

Deploy an LLM using a high-performance framework like vLLM with GPU acceleration.

Deploy an LLM using an enterprise-focused framework like Text Generation Inference (TGI).

Configure core serving parameters: max tokens, temperature, concurrency limits, and batch size.

Load and serve quantized model weights (e.g., GPTQ, AWQ) within a serving framework.

Enable and configure prompt caching to reduce redundant prefill computation.

Expose an OpenAI-compatible API endpoint from a serving framework for standardized integration.

Run and interpret load tests comparing the throughput and latency of different serving frameworks.

Establish and benchmark baseline inference performance (latency, throughput, TTFT) for an un-optimized model.

Apply INT8 and INT4 weight quantization and measure the impact on performance and memory usage.

Compare the accuracy-performance trade-offs of different quantization methods (e.g., GPTQ, AWQ) for a model.

Explain how PagedAttention optimizes dynamic KV cache memory management in vLLM.

Explain the mechanism of speculative decoding, where a draft model generates token candidates for verification.

Configure speculative decoding in a serving framework and benchmark its impact on generation speed.

Configure the NVIDIA Container Toolkit and Kubernetes device plugin to expose GPUs to pods.

Implement a pod placement strategy for GPU nodes using Kubernetes node affinity and taints/tolerations.

Implement request queuing using a message broker (e.g., SQS) to enable asynchronous inference processing.

Implement token-aware rate limiting to manage endpoint usage based on generation load, not just request count.

Design and implement a retry mechanism with exponential backoff for transient inference failures.

Instrument LLM serving endpoints with distributed tracing using OpenTelemetry.

Configure metrics collection for latency percentiles, throughput, error rates, and GPU utilization.

Set up structured logging for inference requests, including token counts and model metadata.

Build an operational monitoring dashboard in Grafana covering latency, throughput, error rates, and GPU metrics.

Track per-request token usage to compute cost-per-request and cost-per-thousand-tokens metrics.

Configure alerts for SLO violations and anomalous inference patterns (e.g., sudden latency increase).

Build a cost analytics dashboard tracking dollars-per-token and dollars-per-request over time.

Implement liveness and readiness probes for LLM serving pods, accounting for long model-loading times.

Implement the circuit breaker pattern to handle failures in downstream model services.

Configure fallback strategies, such as routing to a smaller model or returning a cached response, when primary inference fails.

Implement graceful degradation by routing to a smaller model when P99 latency exceeds its SLO.

Implement graceful degradation by returning a 'system busy' response when queue depth exceeds a threshold.

Configure a model registry for versioning and managing LLM artifacts.

Implement canary deployments for new model versions on Kubernetes with traffic splitting.

Set up automated evaluation gates in a CI/CD pipeline that block promotion based on quality regressions.

Configure automated rollback triggered by either evaluation gate failure or live monitoring SLO violations.

Integrate input and output guardrails to enforce content safety policies and topic restrictions.

Configure fine-grained audit logging to capture prompt/response data and model access patterns for compliance.

Design a role-based access control (RBAC) policy for different models and administrative functions.

Apply standard security best practices (secrets management, mTLS, network policies) to the LLM infrastructure.

Configure a router to direct requests to different LLMs based on task-specific headers or payload fields.

Extend the router to differentiate between and route requests for text and image modalities.

Implement policy-based routing rules that optimize for cost, latency, or model capability.

Identify and formulate key cost drivers for an LLM serving endpoint (GPU-hour cost, per-token cost, data egress).

Build a model to calculate dollars-per-request and dollars-per-successful-task using observability data.

Estimate required inference throughput from user traffic projections and SLOs.

Calculate the required number of GPU instances by mapping throughput requirements to benchmarked GPU performance.

Create a cost projection for planned GPU capacity and validate it against a target budget.

Design a CI/CD pipeline for LLMs, from model artifact ingestion to canary and production deployment.

Script a benchmark evaluation suite that captures performance and quality metrics for a given model.

Integrate the evaluation script into a CI/CD workflow as an automated pass/fail gate.

Build the full CI/CD pipeline that deploys model updates through canary stages in Kubernetes.

Configure a comprehensive monitoring dashboard that integrates latency, cost, GPU, and quality panels.

Configure automated rollback in the pipeline triggered by an evaluation gate failure.

Configure automated alerting and rollback based on live SLO violations from the monitoring system.

Document the pipeline, deployment strategy, and monitoring setup for a new model.