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saravanakumardb1 6d18344fe0 docs(local-llms): add 7 RTX 5090 capability deep-dive guides 
New capabilities/ subfolder with detailed guides:
- 01: GPU inference speed (benchmarks, Ollama tuning, API usage)
- 02: Whisper batch transcription (scripts, Python integration, use cases)
- 03: TTS generation at scale (Orpheus + Qwen3, batch scripts, voice cloning)
- 04: Fine-tuning / training (LoRA, QLoRA, data prep, Ollama export)
- 05: CUDA / TensorRT / ML research (toolchain setup, Triton kernels, profiling)
- 06: Stable Diffusion / image gen (ComfyUI, SDXL, FLUX, batch generation)
- 07: Multi-GPU workloads (scaling path, eGPU, cloud, cost planning)
- README: index with learning order and prerequisites

Each guide covers: what it is, how to use it, benefits, skills to learn
2026-02-21 20:36:21 -08:00
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01-gpu-inference-speed.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00
02-whisper-batch-transcription.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00
03-tts-generation-at-scale.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00
04-fine-tuning-training.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00
05-cuda-tensorrt-ml-research.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00
06-stable-diffusion-image-gen.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00
07-multi-gpu-workloads.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00
README.md docs(local-llms): add 7 RTX 5090 capability deep-dive guides 2026-02-21 20:36:21 -08:00

README.md

RTX 5090 Capabilities — Deep Dive Guides

What you can do with the Razer Blade 18's RTX 5090 (24 GB GDDR7) that you can't (or can't do well) on the Mac.

Each guide covers: what it is → how to use it → real-world use cases → benefits → skills you'll learn.

Guides

# Capability Key Benefit Skill Level
01 GPU Inference Speed 2–4× faster LLM responses Beginner
02 Whisper Batch Transcription Hours of audio in minutes Beginner
03 TTS Generation at Scale Faster-than-realtime voice synthesis Beginner
04 Fine-Tuning / Training Customize models on your own data Intermediate
05 CUDA / TensorRT / ML Research Full NVIDIA ML toolchain Intermediate
06 Stable Diffusion / Image Gen 5–8s per image, unlimited free Beginner
07 Multi-GPU Workloads (Future) Scaling path to production Advanced

Suggested Learning Order

Week 1:  01 (Inference) → 02 (Whisper) → 03 (TTS)
         Get familiar with the GPU, benchmark your models

Week 2:  06 (Stable Diffusion)
         Set up ComfyUI, generate app assets

Week 3:  04 (Fine-Tuning)
         QLoRA your first 7B model on your own code

Week 4:  05 (CUDA / TensorRT)
         Deeper GPU programming, profiling, optimization

Ongoing: 07 (Multi-GPU)
         Reference as you plan scaling

Prerequisites

All guides assume you've completed the Windows setup:

  • NVIDIA drivers installed (Windows)
  • Ollama installed and running (Windows)
  • WSL2 Ubuntu 24.04 set up
  • Repo cloned, setup-tts.sh completed
  • Dashboard running at http://localhost:3000