Distributed GPU Network
DGPUNET (Distributed GPU Network) is a Ray-based computing cluster that pools GPU resources from multiple machines to handle demanding AI workloads. By distributing tasks across consumer-grade GPUs, DGPUNET enables running large models and complex pipelines that would otherwise require expensive enterprise hardware or costly cloud infrastructure.
The cluster orchestrates workloads for SIIMPAF's animation pipeline, including Stable Diffusion image generation, EMAGE body motion synthesis, and PantoMatrix avatar animation. It represents a philosophical statement about accessibility in AI development - democratizing access to computational resources that would otherwise be gatekept by cloud providers.
| Node | System | CPU | GPU | VRAM | RAM | Role |
|---|---|---|---|---|---|---|
| nv5090 | Custom Tower | AMD Ryzen 9 7900X | NVIDIA RTX 5090 | 32GB | 128GB | Head Node |
| nv4090 | Alienware M18R2 | Intel i9 | NVIDIA RTX 4090 Laptop | 16GB | 64GB | Dev Machine |
| nv4080 | Alienware M18R1 | Intel i9 | NVIDIA RTX 4080 Laptop | 12GB | 64GB | Worker |
| nv4070 | Alienware M16R1 | Intel i7 | NVIDIA RTX 4070 Laptop | 8GB | 64GB | Worker |
| nv3090 | Dell XPS Tower | Intel i7 | NVIDIA RTX 3090 | 24GB | 128GB | Worker |
Stable Diffusion inference distributed across available GPU resources for avatar image creation.
EMAGE gesture generation from audio, producing natural body movements for avatar animation.
PantoMatrix/AnimateAnyone rendering combining facial expressions with body motion.
Large language model inference distributed when local VRAM is insufficient. Supports 70-100B+ parameter models.
Frame-by-frame video rendering and post-processing across worker nodes.
LoRA fine-tuning and other training tasks distributed across the cluster.
DGPUNET's distributed GPU architecture makes possible applications that would be impossible on a single machine. RPEPTFS (Role-playing Enhanced Pitch Training Feedback Simulator) is a prime example.
The nv5090's 32GB VRAM can run at most 2 AI investor NPCs simultaneously with real-time animation. Insufficient for realistic panel scenarios.
Distributing LLM inference, TTS, and animation across 5 GPUs enables full investor panels - each NPC with unique personality, real-time responses, and animated avatars.
The Business Impact: Without DGPUNET, RPEPTFS could only show entrepreneurs practicing with 2 investors at a time - far from the realistic 5-6 person investor panels they'll face in real pitch meetings. DGPUNET transforms RPEPTFS from a limited demo into a genuinely useful training tool for startup founders seeking Series A funding.
mistral, llama2, dolphin-mistral, nomic-embed-text, codestralThe following articles document the journey and philosophy behind DGPUNET and related AI infrastructure:
Over the past several months, I've been working on something that started as a practical necessity but evolved into a philosophical statement about accessibility in AI development. When a startup couldn't get anything better than a pitiful G10 GPU instance from their cloud provider - completely insufficient for the machine learning workloads needed - I realized I had to take matters into my own hands...
In 1977, a cousin introduced me to role-playing games. Two years later, another cousin gave me access to the University of Utah's computer network. At 8 or 9 years old, I was online and learning to code. These early experiences with pattern matching, NPC behaviors, and making computers feel responsive laid the foundation for four decades of AI development.
This era focused on infrastructure scaling - IRC bots for natural language processing, Beowulf clusters for distributed computing, building ISPs and data centers. Four significant patterns emerged: commodity over enterprise, distributed over centralized, humanizing computer interaction, and automation with reliability.
Therapeutic gaming, educational technology, and real-time AI systems that outperformed commercial solutions. Applying decades of lessons to professional contexts.
GPU scarcity, centralization concerns, and the decision to build DGPUNET - a distributed GPU infrastructure using consumer hardware and Ray clustering to democratize access to AI computational resources.
Bringing four decades of lessons together in SIIMPAF - a comprehensive self-hosted AI system that embodies the principles of distributed computing, open source, and computational independence.
The journey from IRC bots and NPC behaviors to RPEPTFS - an AI-powered pitch training platform with QLoRA-trained investor NPCs based on real Dragons' Den and Shark Tank personalities.