Scaling AI Solutions In Enterprises

In enterprise AI - '23 was the mad rush to a flashy demo - '24 will be all about getting to real production value Three key steps for this in our experience: - (1) Develop your "micro" benchmarks - (2) Develop your data - (3) Tune your entire LLM system- not just the model 1/ Develop your "micro" benchmarks: - "Macro" benchmarks e.g. public leaderboards dominate the dialogue - But what matters for your use case is a lot narrower - Must be defined iteratively by business/product and data scientist together! Building these "unit tests" is step 1. 2/ Develop your data: - Whether via a prompt or fine-tuning/alignment, the key is the data in, and how you develop it - Develop = label, select/sample, filter, augment, etc. - Simple intuition: would you dump a random pile of books on a student's desk? Data curation is key. 3/ Tune your entire LLM system- not just the model: - AI use cases generally require multi-component LLM systems (eg. LLM + RAG) - These systems have multiple tunable components (eg. LLM, retrieval model, embeddings, etc) - For complex/high value use cases, often all need tuning 4/ For all of these steps, AI data development is at the center of getting good results. Check out how we make this data development programmatic and scalable for real enterprise use cases @SnorkelAI snorkel.ai :)

Optimizing Large Language Models (LLMs) is essential to making AI more sustainable. Some impactful methods include model optimization, hardware optimization, and compression techniques. Model optimization focuses on reducing complexity. Techniques like SparseGPT pruning can achieve high levels of sparsity, reducing computational load without sacrificing accuracy. Quantization further compresses models by lowering precision, allowing for smaller, faster models that still perform well. Hardware optimization leverages specialized accelerators and chip architectures to run sparse models more efficiently. This can significantly improve training and inference speeds, leading to notable energy savings. Compression techniques such as knowledge distillation and low-rank factorization help reduce the model’s size by replicating large models in smaller, efficient versions. This makes them suitable for deployment on resource-constrained devices without significant loss in capability. Optimizing LLMs holistically through these methods is key to creating efficient, high-performing models that align with the principles of Green AI. Some of the research references: 1. SparseGPT Pruning and Compression Techniques for LLMs - https://lnkd.in/d-8dy4YB 2. An Empirical Study of LLaMA3 Quantization: From LLMs to MLLMs - https://lnkd.in/dr75K4vP 3. A Survey on Model Compression for Large Language Models - https://lnkd.in/d3KubdSf

I think that LLM will continue to scale to trillions of parameters, therefore pipeline parallelism (PP) will remain a key strategy for efficient training. PipeOffload unlocks scalable pipeline parallelism with a memory optimization, removing the bottleneck observed in current activation memory overhead. Here’s why this matters: 🔹 Efficient Offloading: Empirical studies show that at least 50% and sometimes 100% of activation memory can be offloaded with negligible performance cost. 🔹 Selective Offload Strategy: When full offload isn’t feasible, prioritizing activations with longer lifespan drastically reduces peak memory, making PP more efficient. 🔹 Breakthrough in PP vs. Tensor Parallelism (TP): By integrating PipeOffload, pure PP becomes a stronger alternative to TP. It delivers up to 19% acceleration with lower memory use, making distributed training more efficient at scale. 🔹 Scalability Insights: With PipeOffload, per-device activation memory scales better, making PP more viable even as model sizes grow. We are now witnessing trade-offs in distributed training, making PP a first-class alternative to TP for large-scale AI workloads. The continued theme for LLMs continues to be more scalability, better performance, optimized computational and memory footprint. #genai #technology #artificialintelligence

If you're an AI Engineer wanting to move out of the simple LLM API calling paradigm and understand how LLM inference actually works, this is a nice starting point. - Explains what LLM inference is, how it differs from training, and how it works. - Covers deployment options like serverless vs. self-hosted, and OpenAI-compatible APIs. - Guides model selection, GPU memory planning, fine-tuning, quantization, and tool integration. - Details advanced inference techniques like batching, KV caching, speculative decoding, and parallelism. - Discusses infrastructure needs, challenges, and trade-offs in building scalable, efficient LLM inference systems. - Emphasizes the importance of observability, cost management, and operations (InferenceOps) for reliability. Link to guide by BentoML: https://bentoml.com/llm/ #AI #LLMs #GenAI

Given a single model, how do we improve an #LLM’s reasoning performance with limited resources 💻 and inference time⌛️? Can a smaller 1.5B model outperform a 7B model without incurring long inference time from sequential queries?  In the work of Gregory Lau, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, et al. that was presented at the #NeurIPS2024 Workshop on Foundation Model Interventions, we introduce the framework called Dipper to create #LLMs ensembles from an optimized set of diverse reasoning prompts to improve performance. Unlike sequential inference time methods, Dipper runs queries in parallel, making it super fast ⏩️ and effective. Furthermore, Dipper can work with LLM APIs without model access 📦! With Dipper, we demonstrated how a small ensemble of just three 1.5B models can outperform a 7B model on MATH, while taking almost the same inference time and just <3x compute for a normal query thanks to accelerated batch inference methods 😱 ! Paper: https://lnkd.in/gXvmh_9X

Breaking: RAG-R1 Framework Revolutionizes How LLMs Handle External Knowledge Researchers from AWorld Team and Inclusion AI have just released RAG-R1, a groundbreaking training framework that fundamentally changes how Large Language Models interact with external knowledge sources during reasoning. The Core Innovation Traditional RAG systems suffer from a critical bottleneck: they generate only single search queries when external retrieval is needed, leading to substantial inference time and limited knowledge acquisition. RAG-R1 solves this with multi-query parallelism - enabling models to generate up to three parallel search queries simultaneously. Under the Hood Architecture The framework operates through a sophisticated two-stage training process: Stage 1: Format Learning SFT - The system generates samples integrating reasoning and search, segmented into four distinct categories. Models learn to respond in a "think-then-search" format using special tokens like <think>, <search>, and <answer> to structure their reasoning process. Stage 2: Retrieval-Augmented RL - Employs Proximal Policy Optimization with outcome-based rewards to enhance reasoning capabilities. The system implements retrieval masked loss to prevent retrieved tokens from interfering with the model's inherent reasoning abilities. Technical Breakthrough The multi-query parallelism returns results in JSON format, clearly aligning search queries with retrieved documents. This approach reduces retrieval rounds by 11.1% while maintaining comparable time per retrieval operation. Performance Impact Testing on seven question-answering benchmarks using Qwen2.5-7B-Instruct as the backbone model showed remarkable results: - Up to 13.2% improvement over strongest baselines - Significant performance gains across both general QA and multi-hop reasoning tasks - Excellent generalization across out-of-domain datasets The framework addresses the fundamental challenge of LLMs generating hallucinated or outdated responses by enabling adaptive leverage of both internal and external knowledge during the reasoning process. This represents a significant step forward in making AI systems more reliable and grounded in real-world knowledge.

The future of MCP is remote — and it's already here. If you’ve been playing with AI agents or LLM tools like Copilot in VS Code... You’ve probably heard of MCP (Model Context Protocol). It’s fast becoming the connective tissue for the modern AI stack. Now imagine this: → Instead of every tool running locally, → You run a remote MCP server — fully serverless. → Hosted on Azure Container Apps. → Secure, scalable, and API-key protected. That’s exactly what Anthony Chu did. ✅ Built a remote MCP server using FastAPI ✅ Added SSE transport support ✅ Protected with API key auth ✅ Deployed it to Azure Container Apps ✅ Hooked it up to VS Code and Copilot Best part? It just works. So if you’re: ☁️ Running AI agents 💬 Building tools for devs 🔐 Exploring secure remote access 🌍 Or want scalable inference endpoints... This guide will help you deploy your own remote MCP server in under an hour. 📖 Full write-up can be found here: https://lnkd.in/gHiBFHAz ♻️ Repost if you’re ready to take your MCP skills cloud-native. #MCP #Serverless #AzureContainerApps #GitHubCopilot #AIInfrastructure #FastAPI #LLM #OpenSource

Are your LLM apps still hallucinating? Zep used to as well—a lot. Here’s how we worked to solve Zep's hallucinations. We've spent a lot of cycles diving into why LLMs hallucinate and experimenting with the most effective techniques to prevent it. Some might sound familiar, but it's the combined approach that really moves the needle. First, why do hallucinations happen? A few core reasons: 🔍 LLMs rely on statistical patterns, not true understanding. 🎲 Responses are based on probabilities, not verified facts. 🤔 No innate ability to differentiate truth from plausible fiction. 📚 Training datasets often include biases, outdated info, or errors. Put simply: LLMs predict the next likely word—they don’t actually "understand" or verify what's accurate. When prompted beyond their knowledge, they creatively fill gaps with plausible (but incorrect) info. ⚠️ Funny if you’re casually chatting—problematic if you're building enterprise apps. So, how do you reduce hallucinations effectively? The #1 technique: grounding the LLM in data. - Use Retrieval-Augmented Generation (RAG) to anchor responses in verified data. - Use long-term memory systems like Zep to ensure the model is always grounded in personalization data: user context, preferences, traits etc - Fine-tune models on domain-specific datasets to improve response consistency and style, although fine-tuning alone typically doesn't add substantial new factual knowledge. - Explicit, clear prompting—avoid ambiguity or unnecessary complexity. - Encourage models to self-verify conclusions when accuracy is essential. - Structure complex tasks with chain-of-thought prompting (COT) to improve outputs or force "none"/unknown responses when necessary. - Strategically tweak model parameters (e.g., temperature, top-p) to limit overly creative outputs. - Post-processing verification for mission-critical outputs, for example, matching to known business states. One technique alone rarely solves hallucinations. For maximum ROI, we've found combining RAG with a robust long-term memory solution (like ours at Zep) is the sweet spot. Systems that ground responses in factual, evolving knowledge significantly outperform. Did I miss any good techniques? What are you doing in your apps?

LLM Cost Optimization Strategies: Achieving Efficient AI Workflows Large Language Models (LLMs) are transforming industries but come with high computational costs. To make AI solutions more scalable and efficient, it's essential to adopt smart cost optimization strategies. 🔑 Key Strategies: 1️⃣ Input Optimization: Refine prompts and prune unnecessary context. 2️⃣ Model Selection: Choose the right-size models for task-specific needs. 3️⃣ Distributed Processing: Improve performance with distributed inference and load balancing. 4️⃣ Model Optimization: Implement quantization and pruning techniques to reduce computational requirements. 5️⃣ Caching Strategy: Use response and embedding caching for faster results. 6️⃣ Output Management: Optimize token limits and enable stream processing. 7️⃣ System Architecture: Enhance efficiency with batch processing and request optimization. By adopting these strategies, organizations can unlock the full potential of LLMs while keeping operational expenses under control. How is your organization managing LLM costs? Let's discuss!

In the rapidly evolving landscape of AI/LLM, a paradigm shift is underway that demands the attention of forward-thinking enterprises: the rise of Domain-Specific Large Language Models. While general-purpose LLMs have captured headlines, it is the targeted power of domain-specific models that is going to reshape the AI landscape. ## Why Domain-Specific LLMs 1. Precision in Specialization:   Domain-specific LLMs offer unparalleled accuracy within their designated fields. By training on curated, industry-specific datasets, these models develop a nuanced understanding of sector-specific terminology, regulations, and best practices. 2. Resource Optimization:   While general-purpose LLMs require vast computational resources, domain-specific models present a more sustainable alternative. Their focused training datasets and narrower scope allow for more efficient use of computational power and data storage. 3. Enhanced Data Governance and Compliance:   In an era of stringent data protection regulations, domain-specific LLMs offer superior control over sensitive information. By limiting the model's exposure to a specific domain, organizations can more effectively manage data access, reducing the risk of inadvertent disclosure. 4. Accelerated Innovation Cycles:   The focused nature of domain-specific LLMs allows for more rapid iteration and deployment of AI solutions. 5. Competitive Differentiation:   By investing in domain-specific LLMs, organizations can develop proprietary AI capabilities that are uniquely tailored to their specific market challenges. ## The Implementation Imperative In our experience implementing domain-specific LLMs, we've observed: - A 40% increase in task-specific accuracy compared to general-purpose models - A 50% reduction in time to deployment for new AI features - A 35% decrease in data processing costs due to more efficient resource utilization This diagram illustrates the flow of how enterprises can use domain-specific LLMs while maintaining security and isolation. Here's a brief explanation of the diagram: 1- Enterprise data is first classified into sensitive and non-sensitive categories. Sensitive data is processed in a secure enclave, where domain-specific LLMs operate. 2- Non-sensitive data can be processed by a general-purpose LLM. 3- Each domain-specific LLM produces isolated outputs. 4- All outputs, including those from the general-purpose LLM, go through a security check. 5- Finally, the verified outputs are integrated and used in various enterprise applications. This flow emphasizes the importance of data security, isolation of domain-specific models, and the integration of outputs from various LLMs.