Faster LLMs: Accelerate Inference with Speculative Decoding

Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdnJta Learn more about AI Inference here → https://ibm.biz/BdnJtG Want faster large language models? 🚀 Isaac Ke explains speculative decoding, a technique that accelerates LLM inference speeds by 2-4x without compromising output quality. Learn how "draft and verify" pairs smaller and larger models to optimize token generation, GPU usage, and resource efficiency. AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdnJtn #llm #aioptimization #machinelearning

Join Today
Understanding LLM Inference | NVIDIA Experts Deconstruct How AI Works
▶︎

Understanding LLM Inference | NVIDIA Experts Deconstruct How AI Works

Storchennest Live Webcam in Bad Salzungen, Thüringen
▶︎

Storchennest Live Webcam in Bad Salzungen, Thüringen

Mastering LLM Inference Optimization From Theory to Cost Effective Deployment: Mark Moyou
▶︎

Mastering LLM Inference Optimization From Theory to Cost Effective Deployment: Mark Moyou

What Is Llama.cpp? The LLM Inference Engine for Local AI
▶︎

What Is Llama.cpp? The LLM Inference Engine for Local AI

Speculative Decoding: When Two LLMs are Faster than One
▶︎

Speculative Decoding: When Two LLMs are Faster than One

How to make LLMs fast: KV Caching, Speculative Decoding, and Multi-Query Attention | Cursor Team
▶︎

How to make LLMs fast: KV Caching, Speculative Decoding, and Multi-Query Attention | Cursor Team

Speculative Decoding: 3× Faster LLM Inference with Zero Quality Loss
▶︎

Speculative Decoding: 3× Faster LLM Inference with Zero Quality Loss

What Are Large Reasoning Models (LRMs)? Smarter AI Beyond LLMs
▶︎

What Are Large Reasoning Models (LRMs)? Smarter AI Beyond LLMs

The Four Types of Memory Every AI Agent Needs
▶︎

The Four Types of Memory Every AI Agent Needs

Why Inference is hard..
▶︎

Why Inference is hard..

This Simple Trick Made ALL LLMs 2x Faster
▶︎

This Simple Trick Made ALL LLMs 2x Faster

How vLLM Became the Standard for Fast AI Inference | Simon Mo, Inferact
▶︎

How vLLM Became the Standard for Fast AI Inference | Simon Mo, Inferact

AI Optimization Lecture 01 - Prefill vs Decode - Mastering LLM Techniques from NVIDIA
▶︎

AI Optimization Lecture 01 - Prefill vs Decode - Mastering LLM Techniques from NVIDIA

RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
▶︎

RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models

Deep Dive: Optimizing LLM inference
▶︎

Deep Dive: Optimizing LLM inference

Speculative Decoding Explained
▶︎

Speculative Decoding Explained

Yann LeCun's $1B Bet Against LLMs
▶︎

Yann LeCun's $1B Bet Against LLMs

Exploring the Latency/Throughput & Cost Space for LLM Inference // Timothée Lacroix // CTO Mistral
▶︎

Exploring the Latency/Throughput & Cost Space for LLM Inference // Timothée Lacroix // CTO Mistral

Most devs don't understand how LLM tokens work
▶︎

Most devs don't understand how LLM tokens work

Understanding the LLM Inference Workload - Mark Moyou, NVIDIA
▶︎

Understanding the LLM Inference Workload - Mark Moyou, NVIDIA