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A real world system design: the hard part of AI real-estate creative isn’t the AI
A reference architecture for generating listing ads that won’t trip Fair Housing or a state disclosure rule. This architecture is coming from a real world use-case which anonymized and by the company and industry. Most write-ups about generative marketing spend their wordcount on the model. Which checkpoint, which sampler, how many steps. That’s the easy…
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vLLM vs TensorRT-LLM vs Ray Serve: A Stack, Not a Showdown
Two of them run your model. One of them runs a fleet of the other two. Here’s how the layering actually works — and how to choose. I keep seeing this comparison framed as a three-way cage match — pick vLLM or TensorRT-LLM or Ray Serve, may the best framework win. And every time, I…
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Beyond GPUs: Mastering Ultra-Scale LLM Training – Part 2
Introduction In the first part of this series, we unpacked the big picture of scaling LLM training. The “why” and “what” behind ultra-scale setups, and how different forms of parallelism come together to make training trillion-parameter models even possible. That gave us the map. Now it’s time to get into the weeds of the “how.”…
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Beyond GPUs: Mastering Ultra-Scale LLM Training – Part 1
Introduction Training today’s largest language models demands massive computational resources, often thousands of GPUs humming in perfect harmony, orchestrated to act as one. Until recently, only a few elite research labs could marshal such “symphonies” of compute power. The open-source movement has started to change that by releasing model weights (like Llama or DeepSeek) and…
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Building a High-Quality RAG System: Challenges and Solutions
In the fast-evolving field of AI, Retrieval-Augmented Generation (RAG) has become a standout technique by effectively bridging the gap between information retrieval and text generation. Essentially, a RAG system retrieves relevant documents from a large corpus in response to a user query, then uses a generative model to produce a coherent response grounded in the…
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Supercharging Your Inference of Large Language Models with vLLM (part-2)
As discussed in part 1 of this blog post vLLM is a high-throughput distributed system for serving large language models (LLMs) efficiently. It addresses the challenge of memory management in LLM serving systems by introducing PagedAttention, an innovative attention algorithm inspired by virtual memory techniques in operating systems. This approach allows for near-zero waste in…
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Supercharging Your Inference of Large Language Models with vLLM (part-1)
As the demand for large language models (LLMs) continues to rise, optimizing inference performance becomes crucial. vLLM is an innovative library designed to enhance the efficiency and speed of LLM inference and serving. This blog post explains a high level view of vLLM’s capabilities, its unique features, and how it compares to similar solutions in…
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Challenges and Best Practices in Developing Multi-Agent AI Applications
The development of multi-agent AI applications, especially those leveraging large language models (LLMs), involves navigating numerous challenges. Ensuring these systems perform optimally requires a blend of strategic planning, robust design principles, and advanced monitoring techniques. Here, we delve into the challenges, best practices, and recommendations for better developing and deploying multi-agent AI applications. Challenges in…
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Deploying Agentic Systems: Navigating the Complexities of Multi-Agent LLM Applications
Image source Introduction Deploying agentic Large Language Model (LLM) systems is a multifaceted challenge, involving intricate multi-agent coordination, scalability, and real-time processing. As organizations increasingly depend on LLMs for tasks such as customer service automation and data analysis, ensuring seamless and efficient operation becomes paramount. The complexity lies in managing interactions between multiple agents, each…
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Ali Darbehani 