Gourav Jain

Quantization in Large Language Models (LLMs) As LLMs scale, intelligence is no longer the bottleneck — memory, latency, and cost are. Quantization turns out to be one of the most powerful (and misunderstood) techniques enabling real-world deployment of large models. In this blog, I break down: • Why LLMs are surprisingly quantization-friendly • INT8 vs INT4 vs NF4 — what actually works in practice • How modern architectures absorb low-bit noise • Where quantization fails and how to design around it Quantization is no longer an optimization step — it’s foundational infrastructure for production LLM systems. Link: https://lnkd.in/gqGje75G #LLMs #AIEngineering #Quantization #MachineLearning #GenAI #MLOps

9

2 Comments

Why MCP Matters for the Future of AI As LLMs move from demos to real-world systems, the biggest challenge isn’t model capability—it’s context, action, and integration at scale. In this blog, I break down Model Context Protocol (MCP): • Why prompt-based integrations don’t scale • How MCP solves the M×N integration problem • Why MCP is emerging as the operating system for AI agents If you’re building agentic systems, RAG pipelines, or enterprise AI, MCP is a concept worth understanding early. 🔗 Read the blog here: https://lnkd.in/gJpcAHdt #AI #LLM #MCP #AgenticAI #Architecture #FutureOfAI

4

We’re proud to partner with Groq to power enterprise AI agents with low-latency inference for performance-critical workloads. Teams on StackAI deploy Groq for latency-sensitive workflows: high-volume document analysis, customer-facing interactions, and internal knowledge retrieval. In each of these situations, fast, reliable responses materially improve user experience and productivity. StackAI and Groq are a prime example of how matching the right inference provider to the right workload helps enterprises build AI agents that perform in real production environments. Want to learn more? Read the full piece at the link in comments.  #StackAI #Groq #EnterpriseAI #AIagents

33

4 Comments

Get faster and smarter MOE inference straight out of the box. 👇Deep dive on scaling expert parallelism with TensorRT-LLM. LLMs with MOE promise higher model capacity without linearly increasing compute costs - but they introduce new challenges -- more conditional computation, dynamic routing, and non-uniform GPU utilization -- solved by TensorRT-LLM. ✨ New in TensorRT-LLM has native support for expert parallelism—designed for fast, efficient inference with MoE models like Mixtral (Mistral AI) and DeepSeek (DeepSeek AI). This gives you: ✅ Dynamic expert routing: Automatically route tokens to the top-k experts with minimal overhead. ✅ Efficient expert scheduling: Balance expert loads across GPUs using smart sharding and token bucketization. ✅ Memory-aware execution: Maximize hardware utilization while respecting memory budgets. ✅ Drop-in support: Use @HuggingFace models with minimal code changes via TensorRT-LLM's #Python API. 🧠 How it works: MoE models activate only a subset of "experts" for each token. This dynamic nature is powerful—but hard to optimize. It’s all done under the hood using custom #CUDA kernels and NCCL-based communication primitives—giving you low latency, high throughput, and better GPU scaling. ✨ TensorRT-LLM handles: ✅Token-expert mapping using the gating network. ✅Token sorting to batch same-expert tokens together. ✅Expert parallel execution across GPUs. ✅Merging outputs for final predictions. 🛠️ Developer workflow - here is the code to get started. # Clone the repo git clone https://lnkd.in/g-GiDX23 # Use included examples to load and run a Mixtral model cd TensorRT-LLM/examples/mixtral From there, the Python API lets you load the model, convert it with TensorRT, and run expert parallel inference—all with a few lines of code. Results? 📈 Performance at scale. Tests show up to 2.3x faster inference throughput compared to standard tensor parallelism when using 8 GPUs and top-2 experts per token. Even better—TensorRT-LLM keeps efficiency high across increasing batch sizes. Want to see it in action or contribute? 👉 Read the full tech blog: https://lnkd.in/g_7YV3vV 👉 Explore the code on GitHub: https://lnkd.in/gNjQ5W2U 👉 Follow updates in the TensorRT-LLM repo: https://lnkd.in/gqSHYQ4u Share your experiences with us.

78

3 Comments

Manual documentation can’t keep up with modern data pipelines. Traxccel automates the process, using LLMs and Databricks metadata to generate clean, version-controlled docs embedded into your CI/CD workflows. The result is scalable, audit-ready documentation that’s always in sync with your data logic. Read the blog and see why it’s redefining documentation across the enterprise: https://lnkd.in/dkdXEsBq #ETLautomation  #DataEngineering #LLM #Databricks #TechnicalDocumentation  #axlPULSE #Traxccel 

7

As Databricks adoption accelerates, platform leaders face mounting pressure to deliver reliable performance, predictable costs, and scalable data operations. But with a fragmented vendor landscape and no one-size-fits-all approach, selecting the right observability solution can feel overwhelming. That's why we created the Databricks Data Observability Buyer's Guide - a clear, actionable framework to help you cut through the noise. 📥 Download your free copy today: https://lnkd.in/gayN9ui9 #Databricks #DataObservability #FinOps #AIOptimization #CloudCosts

4

🚀 Making Large Language Models Faster and Smarter Today I read a research paper by Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, and Sanjiv Kumar, titled “Faster Cascades via Speculative Decoding.” The authors propose speculative cascading, which integrates cascade deferral rules into speculative execution — blending the efficiency of cascades (small model first, defer to large when needed) with the speed of speculative decoding (draft + parallel verification). 🔑 Key findings: Achieves better cost–quality trade-offs than either cascades or speculative decoding alone. Tested on T5 and Gemma models across tasks like summarization, translation, reasoning, and coding. The token-level variant delivers the best trade-off, improving quality while keeping inference efficient. 💡 Why it matters: As LLMs grow larger, speculative cascading offers a practical path to make high-quality inference both faster and more cost-effective. 👉 Question: If efficiency techniques like this become standard, do you think we’ll see more smaller models deployed smartly—or will industry still rely on ever-larger LLMs? 👉 Research paper link in comments. #AI #LLMs #Efficiency #Research

1

1 Comment

A Thought Exercise : Context Engineering with LLMs is analogous to Feature Engineering in Classical ML What are the similatieties and differences ? Share in the thread #AImusings courtesy: Jim Hong :)

28

9 Comments

If your team is building with LLMs, you know the value of clean, retrievable data. That's why we're teaming up with Unstructured to streamline one of the hardest parts of AI development. This integration removes friction across the AI stack—from unstructured ingestion to semantic retrieval—so your teams can focus on building. Read more from our Product Marketing Managers, Rini Vasan and Jim Allen Wallace, and Unstructured’s Head of Developer Relations, Maria Khalusova here: https://lnkd.in/e98QqXAB

43