blog

What's happening at DeepModel.

Featured

Tuesday, February 11, 2025

Understanding LLM Benchmarks

Explore the world of Large Language Model (LLM) benchmarks and how they evaluate the performance of advanced AI models. This post covers key benchmarks like MMLU, BIG-Bench, HumanEval, TruthfulQA, and MTEB, providing insights into their strengths and limitations. Learn how these benchmarks assess tasks like general knowledge, code generation, truthfulness, and text embeddings. Understand the challenges LLMs face in reasoning, ethical judgment, and domain-specific knowledge. Stay ahead in AI development by understanding the future of LLM evaluation.

Himakara Pieris

Wednesday, February 12, 2025

Building an AI Agent Deployment Roadmap That Delivers Real Business Value

Learn how to build a scalable AI agent deployment roadmap that drives real business value. This guide covers AI strategy, agent use case prioritization, governance, AI infrastructure, and scaling AI operations. Discover the seven key workstreams essential for successfully deploying AI agents—ensuring alignment with business goals, risk management, and seamless integration into enterprise workflows. Perfect for CIOs, AI leaders, and businesses looking to move beyond AI experimentation and achieve enterprise-wide AI automation.

Wednesday, February 12, 2025

Reducing the Cost of Inference in Large Language Models (LLMs)

Discover the most effective techniques to reduce inference costs in Large Language Models (LLMs) without sacrificing performance. This in-depth guide explores optimization strategies such as memory-efficient architectures (vLLM, DeepSpeed, FlexGen), speculative decoding, long-context handling (InfLLM), and hardware-aware execution (FasterTransformer, FlashAttention). Learn how quantization, parallelism, offloading, and cost-aware routing (FrugalGPT, TGI) can make LLM inference more affordable and scalable. Whether you're deploying AI models in production or optimizing cloud costs, these expert strategies will help you achieve faster, more efficient, and budget-friendly LLM inference.

Monday, January 20, 2025

Himakara Pieris

The Seven Enterprise AI Challenges

There are four ways to adapt AI to work for your business. I call this the AI ladder. Most organizations will use the top two steps of this ladder: retrieval augmented generation (RAG)and prompt engineering. And, many will find the best effort/ reward ratio with RAG.