LLM OPS & DEPLOYMENT

Table of Contents

• Module 1: The LLM Ops // Models as Production Systems
• Module 2: First Principles // Model Lifecycle & Control Planes
• Module 3: Model Packaging & Artifact Management
• Module 4: Deployment Architectures // Inference at Scale
• Module 5: Runtime Optimization & Acceleration
• Module 6: Observability, Drift & Feedback Loops
• Module 7: Security, Governance & Access Control
• Module 8: Cost, Capacity & Throughput Engineering
• Module 9: Failure Domains & Rollback Strategies
• Module 10: Decision Framework // Strategic Validation
• Frequently Asked Questions (FAQ)
• Additional Resources

Architect’s Summary: This guide provides a deep technical breakdown of LLM Operations (LLM Ops). It shifts the focus from experimental notebooks to deterministic production environments. Specifically, it is written for platform architects, SREs, and AI engineers designing the control planes required to govern, scale, and secure Large Language Models in the enterprise.

Module 1: The LLM Ops // Models as Production Systems

Specifically, LLMs are no longer isolated experiments; they have evolved into long-lived production services that serve as customer-facing interfaces and critical decision-making components. Unlike traditional software, an LLM’s output is probabilistic, making deterministic operational control mandatory for trust. Initially, LLM Ops transforms these models from static, “black-box” weights into managed runtime systems that adhere to enterprise standards for versioning, governance, and availability.

Architectural Implication: If you cannot operate an LLM deterministically, you cannot trust it for regulated data processing. If your deployment lacks an automated path for updates and rollbacks, you are creating a “Legacy AI” liability. Consequently, architects must treat the LLM as a first-class production citizen with the same rigor applied to core transactional databases.

Module 2: First Principles // Model Lifecycle & Control Planes

To master this strategy, you must recognize that the control plane—the logic that routes, monitors, and secures the model—matters more than the model weights themselves.

• Lifecycle Continuity: Initially, the lifecycle spans from model selection and fine-tuning to deployment, monitoring, and eventual retirement.
• Operational Explainability: Specifically, you must be able to audit exactly which version of a model (and which prompt template) generated a specific output.
• Retraining Triggers: Furthermore, the system must detect when model performance degrades and automatically trigger feedback or retraining loops.

Architectural Implication: Without lifecycle control, teams deploy “Ghost Intelligence” that cannot be explained or rolled back. Initially, you must establish a centralized control plane to prevent fragmented, “shadow AI” deployments across the organization.

Module 3: Model Packaging & Artifact Management

Models must be treated as immutable infrastructure artifacts to ensure they can be deployed consistently across diverse environments.

Key Components of an AI Artifact:

• Weights: The large numerical tensors that define the model.
• Tokenizers: The logic that converts text into the numerical format the model understands.
• Configurations & Prompts: Initially, the “System Prompts” that define the model’s behavior and constraints.
• Runtime Dependencies: Specifically, the versions of Python, PyTorch, or CUDA required for execution.

Architectural Implication: You should utilize OCI-compliant model images and cryptographic signing. Initially, model registries must act as the “Single Source of Truth.” Consequently, if an artifact is not signed and versioned, it should never be permitted into the production inference path.

Module 4: Deployment Architectures // Inference at Scale

The choice of deployment architecture determines the balance between latency, throughput, and operational cost.

• Single-Node Inference: Initially, the simplest model; offers low latency but is limited by the VRAM of a single GPU.
• Distributed Inference: Specifically, using Tensor or Pipeline Parallelism to split a model across multiple GPUs or nodes. Mandatory for large models (e.g., Llama-3 70B+).
• Kubernetes-Based Serving: Furthermore, leveraging K8s for auto-scaling, canary releases, and multi-tenant isolation.

Architectural Implication: Architecture dictates reliability. Initially, distributed inference introduces a heavy dependency on the GPU Fabric. Consequently, any latency spike in your RDMA network will manifest as a significant delay in “Time to First Token” (TTFT) for the user.

Module 5: Runtime Optimization & Acceleration

Inference efficiency is primarily a systems engineering problem; optimization often yields larger performance gains than simply buying more hardware.

• Quantization: Initially, converting model weights from FP32/FP16 to INT8 or INT4 to reduce memory footprint and increase speed.
• KV-Cache Reuse: Specifically, optimizing how the model “remembers” previous tokens during a conversation.
• Kernel Fusion: Furthermore, combining multiple GPU operations into a single execution to reduce overhead.

Architectural Implication: You must implement high-performance acceleration stacks like NVIDIA TensorRT-LLM or vLLM. Initially, these engines provide the “Request Batching” required to maintain high throughput under load. Therefore, optimization is the primary lever for controlling AI OpEx.

Module 6: Observability, Drift & Feedback Loops

LLMs do not crash in traditional ways; they degrade silently through “Output Drift” and decreasing accuracy.

Critical Signals:

• Token Throughput: The rate at which the model generates intelligence.
• TTFT (Time to First Token): The primary metric for perceived user latency.
• Prompt Distribution: Initially, monitoring if users are asking questions the model wasn’t designed for.
• Semantic Drift: Specifically, measuring if model answers are straying from grounded truth over time.

Architectural Implication: Without observability, your LLM is an unpredictable black box. Initially, you must implement Human-in-the-Loop (HITL) validation to audit probabilistic outputs. Consequently, observability data must feed directly into the automated retraining triggers defined in Module 2.

Module 7: Security, Governance & Access Control

LLMs significantly expand the corporate attack surface, introducing risks that traditional firewalls cannot mitigate.

Key Security Domains:

• Prompt Injection: Attackers tricking the model into bypassing safety guardrails.
• Data Exfiltration: Models accidentally revealing sensitive PII or IP from their training data.
• Unauthorized Access: Specifically, ensuring that only authorized applications can call high-cost inference endpoints.

Architectural Implication: LLM Ops must enforce Zero Trust for Intelligence. Initially, you must implement RBAC on all inference endpoints and provide automated Prompt Validation layers. Furthermore, every input and output must be logged for forensic and compliance purposes.

Module 8: Cost, Capacity & Throughput Engineering

LLMs are cost-elastic systems; without strict capacity engineering, AI infrastructure costs can scale faster than business value.

Architectural Implication: Token economics replace VM economics in the AI era. Initially, you must implement Tiered Model Routing—sending simple requests to small, cheap models (like Llama-3 8B) and complex requests to expensive models (like GPT-4). Specifically, utilize Request Caching to avoid re-generating tokens for identical queries. Consequently, your architecture must provide the visibility required to charge back costs to specific business units.

Module 9: Failure Domains & Rollback Strategies

AI models fail through regressions and “hallucination amplification,” requiring specialized containment strategies.

• Canary Deployments: Initially, routing 5% of traffic to a new model version to validate performance before a full rollout.
• Shadow Inference: Specifically, running a new model in parallel with the old one to compare outputs without impacting the user.
• Instant Rollback: Furthermore, maintaining versioned artifact pointers to allow for a 1-second revert if a model begins producing toxic or incorrect results.

Architectural Implication: Failure containment is mandatory for enterprise trust. Initially, you must assume that every model update carries the risk of regression. Therefore, the ability to “un-deploy” intelligence is as important as the ability to deploy it.

Module 10: Decision Framework // Strategic Validation

Ultimately, there is no universal LLM Ops stack; the strategy must align with your specific scale, budget, and regulatory constraints.

Choose an LLM Ops model based on your model churn frequency and latency sensitivity. If your industry is highly regulated, a Sovereign LLM Ops stack—where all weights, registries, and inference engines are hosted on-premises—is a requirement. Furthermore, factor in the cost of GPUs; if your budget is tight, prioritize Inference Optimization (Module 5) over horizontal scaling. Consequently, your operational model must be flexible enough to swap models as the underlying AI landscape evolves.

Frequently Asked Questions (FAQ)

Q: Is LLM Ops different from traditional MLOps?

A: Yes. Initially, MLOps focuses on training pipelines and tabular data. LLM Ops emphasizes inference governance, prompt engineering, and the runtime economics of token-based models.

Q: Should we run our LLMs inside Kubernetes?

A: Specifically, yes for most use cases. Kubernetes provides the scaling, isolation, and lifecycle management required for modern serving frameworks like vLLM or Triton.

Q: What is the most common cause of failure in production AI?

A: Initially, it is a lack of observability. Without a way to detect “Semantic Drift,” teams often don’t realize their model is providing incorrect information until a customer reports it.

Additional Resources: