Phase 8: Serverless GPU Deployment & Infrastructure · 50 min · Python · Hugging Face Transformers

GPU Resource Management

H100 for images, H200 for video, B200 for multi-model parallel. NVFP4 quantization: 3x faster, 60% less VRAM. Know your GPUs.

What you will learn

Select GPUs by modality: H100 80GB (images), H200 141GB (video), B200 (multi-model parallel)
Manage VRAM: model loading, offloading, concurrent request handling
Apply quantization: NVFP4 (4-bit) and NVFP8 (8-bit) — 3x faster, 60% less VRAM, minimal quality loss
Optimize batch processing and cold starts: warm pools, model preloading, keep-alive

Objective

Operate media generation under real packaging, cold-start, storage, throughput, and cost constraints. In the paired studio lab, build an analysis tool that measures VRAM usage, inference time, and output quality for FLUX.1 Schnell at different configurations: (1) full precision (fp16), (2) quantized (8-bit), (3) quantized (4-bit). Compare VRAM usage, generation time, and image quality (CLIPScore) for each configuration.

The Problem

A team needs to select GPUs for different generative media workloads:

Image generation (FLUX, SDXL): needs 20-40GB VRAM, 5-15s per image
Video generation (Runway, Kling): needs 60-80GB VRAM, 60-180s per video
Multi-model parallel (image + video + audio): needs 100+GB VRAM
Batch processing (1000 images): needs efficient VRAM management

Choosing the wrong GPU means OOM errors, slow generation, or wasted money.

What you'll build
Select GPUs by modality (H100 for images, H200 for video, B200 for multi-model), manage VRAM (loading, offloading, concurrent requests), apply quantization (NVFP4/NVFP8), and optimize cold starts with warm pools.

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Key terms in this lesson

Quantization