Large language models burn through GPU memory and compute faster than most teams expect. Every prompt creates key-value tensors that sit in GPU memory, and that memory footprint grows with every token and every user. In this article, I walk through what is really happening inside KV cache systems and why architectures like vLLM and LMCache exist in the first place. Instead of treating caching as a performance trick, the post looks at it as a memory strategy that changes how inference systems are built.

This topic matters right now because the economics of AI are shifting. Training made the headlines, but inference is what drives ongoing cost in production systems. Techniques such as KV cache reuse, memory tiering, and shared storage are becoming critical for controlling GPU spend and data center power consumption. As companies deploy chat systems, RAG pipelines, and agent workflows at scale, engineering the inference stack is becoming more important than adding more GPUs.

Dive into the full article here: https://bit.ly/4bl87kn

Bigger models used to win headlines. Now they win (in not good ways) with power bills. This post looks at what changed after DeepSeek R1 made it clear that smarter engineering can compete with brute force. Instead of chasing parameter counts, we look at quantization, fine-tuning, and specialized Small Language Models that focus on one job and do it well. We also unpack what this means for agentic systems, where multiple focused models collaborate instead of one giant model trying to do everything.

This shift is happening for a reason. GPU costs are rising, data center power demand keeps climbing, and inference is now the line item that finance teams watch closely as token costs rise. NVIDIA’s recent inference-focused deal with Groq signals the same trend: latency, efficiency, and cost per token matter more than raw size. If you are building AI systems today, the question is no longer how big your model is. It is how much value it delivers per watt and per dollar.

Dive into the full article on the Open Data Science Conference (ODSC) blog: https://bit.ly/4s6iKye

If you’ve been building RAG systems and something feels off, this post explains why. It picks up where earlier discussions left off and looks at what happens when retrieval stops being something you can inspect or control. The focus is on how teams actually guide AI answers in practice, not by adding more embeddings, but by rethinking retrieval as a first-class part of the system. Along the way, it contrasts vector-heavy approaches with graph-style thinking and introduces the idea of a BM25-based Document RAG Agent as a practical way to regain visibility into how answers are formed.

This topic matters right now because GraphRAG has taken off fast, and for good reason, but many teams are realizing that managing graph schemas, ontologies, and lifecycle rules is a serious commitment. At the same time, pure VectorRAG often feels too fuzzy when correctness and audits matter. The BM25-based Document RAG Agent sits in the middle, borrowing structure from GraphRAG without the full overhead, and grounding retrieval in signals people already understand. As AI systems move from demos to production, especially in regulated or high-risk environments, this kind of tradeoff is becoming a daily decision point for teams trying to ship systems they can explain, debug, and trust.

Dive into the full article here: https://bit.ly/4pz0D3b.