The LLM (Large Language Model) stack refers to the comprehensive set of technologies, tools, and processes used to build, deploy, scale, and maintain applications powered by large language models. This stack has evolved rapidly with the rise of generative AI, addressing unique challenges like high computational demands, data management, model unpredictability, and security concerns. It encompasses everything from foundational hardware to high-level application orchestration, enabling developers to create robust AI systems for tasks such as natural language processing, content generation, and autonomous agents.
The LLM stack operates as a pipeline: Data from pipelines is used to train or fine-tune foundation models on specialized hardware. Trained models are deployed for inference, where orchestration tools like LangChain build complex workflows, often incorporating RAG for accuracy. Security and validation layers filter inputs/outputs, while observability monitors everything to feed back into testing for continuous improvement. For example, in a chatbot app, user queries hit the orchestration layer, retrieve context from vector DBs, run through inference on Groq hardware, get validated by Guardrails, and are logged via Helicone for analysis.
7 Layers of LLM stack:
1. Lower layers (1-3) focus on building reliable models from huge, clean data.
2. Middle layers (4-5) handle reasoning chains and efficient, safe model serving.
3. Top layers (6-7) make LLM power accessible within real-world software and business context.
Comparing the 7 Layers of the LLM Stack:
Here's a side-by-side summary of each layer in the typical LLM stack, highlighting the key components and how each layer connects to the full lifecycle of a large language model, from data to deployed applications.
| Layer | Purpose | Key Components |
|---|---|---|
| 1. Data Acquisition | Gather vast language data to build and train LLMs | - Web crawlers, APIs - Document parsers - Data labeling/curation - Data storage/data lakes |
| 2. Data Preprocessing & Management | Clean, filter, structure and govern the data | - Deduplication, normalization - Tokenization, vocabulary creation - PII removal, privacy tools - Versioned datasets |
| 3. Model Selection & Training | Build, optimize, and train LLMs | - Model architecture (e.g. Transformer variants) - Distributed training frameworks - GPUs/TPUs and accelerators - Checkpointing, early stopping - Fine-tuning, RLHF |
| 4. Orchestration & Pipelines | Enable modular workflows and complex reasoning | - Agent frameworks (e.g., LangChain, Haystack) - Memory modules, scratchpads - Chaining multiple models/tools - Retrieval- Augmented Generation (RAG) pipelines |
| 5. Inference & Execution | Run models at scale, safely & efficiently | - Serving endpoints/APIs - Prompt management/templating - Guardrails for safety & content filtering - Caching & load balancing |
| 6. Integration Layer | Connect LLMs with business systems and data | - API gateways, SDKs - Authentication/authorization modules - Plugins & connectors (e.g., Salesforce, SQL DB) - Usage metering, monitoring |
| 7. Application Layer | Deliver user-facing solutions and automation | - Chatbots, copilots, text analytics - Custom enterprise apps - Reporting/analytics dashboards - Workflow automation |
ref:
Decoding the LLM stack for future AI applications @ https://medium.com/@KapilDaga/decoding-the-llm-stack-for-future-ai-applications-97e5250dbc79
