Shared vs Private LLMs: Cut Latency, Costs & Gain Control | Predibase Inference Engine Deep Dive

Predibase by Rubrik ยท Beginner ยท๐Ÿง  Large Language Models ยท10mo ago
๐Ÿ”” SUBSCRIBE for the latest on LLM fine-tuning, AI scaling, and reinforcement learning hacks! ๐Ÿ‘‰https://www.youtube.com/@Predibase Thinking about scaling your open-source LLMs? This webinar is your blueprint. Discover: โœ… Why shared LLM endpoints break under production traffic โœ… How private managed deployments outperform on latency, cost & security โœ… Real-world benchmarks (LLaMA 3.1 vs GPT-4) โœ… How Turbo + FP8 quantization gives you 4x throughput โœ… How customers like Checkr deliver sub-second inference with 5x cost savings ๐ŸŽฏ Ideal for ML engineers, data scientists, and infra teams building โ€ฆ
Watch on YouTube โ†— (saves to browser)

Chapters (21)

Introduction: Shared vs Private LLM Deployments
1:10 Why Open Source LLMs Are Catching Up
3:00 Fine-Tuning Small Models to Outperform GPT-4
4:50 The Case for Private Managed Endpoints
7:00 Comparing Shared vs Dedicated Infrastructure
9:25 4 Big Problems with Shared LLM Endpoints
13:10 The Privacy Advantage of VPC + Direct Ingress
17:30 Network Architecture: Control Plane vs Data Plane
20:15 Cost Comparison: LLaMA 3.1 vs GPT-4
23:00 Reliability & SLA Benefits with Dedicated Deployments
25:00 Hardware Customization & Accelerator Options
27:15 Turbo + FP8 = 4x Faster Throughput
30:00 Dynamic Adapter Switching with Lorax
32:45 Latency Benchmarks: Predibase vs Fireworks vs DIY
34:40 How to Set Up a Private Deployment in Predibase
38:10 Observability & Monitoring Tools
41:20 SDK Deployment Example
42:30 Real-World Case Study: Checkr
45:00 Q&A: Speculators, VPC, Model Bring-Your-Own, More
50:30 Cost Models for Startups vs Enterprise
53:00 Fine-Tuning & RFT Support at Predibase
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