Shared vs Private LLMs: Cut Latency, Costs & Gain Control | Predibase Inference Engine Deep Dive
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Thinking about scaling your open-source LLMs? This webinar is your blueprint.
Discover:
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Why shared LLM endpoints break under production traffic
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How private managed deployments outperform on latency, cost & security
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Real-world benchmarks (LLaMA 3.1 vs GPT-4)
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How Turbo + FP8 quantization gives you 4x throughput
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How customers like Checkr deliver sub-second inference with 5x cost savings
๐ฏ Ideal for ML engineers, data scientists, and infra teams building โฆ
Watch on YouTube โ
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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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