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Architecting Enterprise LLMOps: Scalable RAG Pipelines, Quantization & Low-Latency Serving

Transitioning generative foundation models into enterprise production requires robust LLMOps architecture: orchestrating continuous embedding pipelines, hybrid retrieval-augmented generation (RAG), and ultra-low-latency GPU inference serving.

1. Hybrid Retrieval-Augmented Generation (RAG)

Mitigate model hallucinations by anchoring generation to authoritative vector stores. Combining dense semantic embeddings with sparse BM25 lexical search provides pinpoint factual context for complex enterprise queries.

2. High-Throughput Inference via PagedAttention & Quantization

Maximize GPU utilization using dynamic memory allocation frameworks such as vLLM and TensorRT-LLM. Applying INT8/INT4 post-training quantization cuts memory footprints in half while maintaining near-lossless perplexity scores.

3. Next-Generation AI Horizons

Stay ahead of on-device LLMs and multimodal agent workflows by exploring the curated tools and briefings at apps.fredric18.uk.

Posted:

2026-09-03

Category: MLOps & LLMOps

Author:

fredric

Tags:

AIArchitecture, ArtificialIntelligence, English, FineTuning, Fredric18, HuggingFace, InferenceOptimization, LargeLanguageModels, LLM, LLMOps, MLOps, Quantization, RAG, VectorDB, vLLM

Architecting Enterprise LLMOps: Scalable RAG Pipelines, Quantization & Low-Latency Serving

1. Hybrid Retrieval-Augmented Generation (RAG)

2. High-Throughput Inference via PagedAttention & Quantization

3. Next-Generation AI Horizons

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