LocalAI GPU Runtime.
Supercharged by Ada Lovelace & Blackwell.
Low-latency local inference, zero-fragmentation Paged KV-Cache, and hardware-aware quantization for GeForce RTX and DGX-class client systems.
Architected by Pavan Kumar Sadashiv to engineer next-generation client-side inference, CUDA Graph replay, and sub-millisecond streaming token generation.
Interactive Local AI Inference Workbench
Real-time local streaming execution with live hardware telemetry on Port 5676.
Physical VRAM Paged KV-Cache Block Map
2,048 Physical Virtual Blocks (16 Tokens / Block | Zero Fragmentation Standard)
Performance-Accuracy Pareto Analytics
Automated sweeps across precision bitwidths, CUDA graph toggles, and speculative policy modes
Decoding Latency per Token (TPOT in ms)
VRAM Footprint Breakdown (MB)
Accuracy-Performance Pareto Sweep Table
| Configuration | Precision | CUDA Graphs | Speculative | TPOT (ms) | VRAM (MB) | Perplexity | SNR (dB) | Pareto Status |
|---|---|---|---|---|---|---|---|---|
| Llama-3.1-8B-AWQ | INT4_AWQ | ON | ON | 0.65 ms | 5,014 MB | 5.60 (+0.18) | 29.4 dB | Optimal (Max Latency Speed) |
| Llama-3.1-8B-FP8 | FP8_E4M3 | ON | OFF | 1.12 ms | 8,829 MB | 5.46 (+0.04) | 38.6 dB | Optimal (Ada Native Precision) |
| Llama-3.1-8B-FP16 | FP16 | OFF | OFF | 1.79 ms | 16,458 MB | 5.42 (Base) | 48.2 dB | Baseline Reference |
NVIDIA Project G-Assist & LoreMaster Gaming Copilot
Powered by mwtuni/loremaster architecture — talk to in-game characters via voice, analyze active screen puzzles with on-device VLMs, and receive sub-millisecond game-loop responses via RTX-LocalAI.
"Wake the hell up, samurai. Arasaka’s subnet security is sloppy on the 48th floor. We breach the ICE through the service elevator, flatline their netrunners, and burn the data to the ground."
ElevenLabs Systems Voice Walkthrough & Explanation Hub
Listen to executive architectural briefings and deep-dive technical explanations voiced by ultra-realistic ElevenLabs AI models.
RULE BREAKING: Engineering Systems & Autonomous AI Architectures
Authored by Founder & Managing Director Pavan Kumar Sadashiv. Establishing verifiable standards across dual-timescale hierarchical reinforcement learning, GPU memory paging algorithms, and enterprise digital systems.
The 100x AI Chief Architect Manifesto
Complete master engineering manifesto and patent disclosure covering C++ Metal/CUDA DaVinci Resolve OpenFX volumetric compute engines, NP-hard CSP Backtracking solvers, and autonomous multi-agent fleet orchestration.
Download Volume 4.0 (Master PDF)HRL Programming Language for LLMs
Domain-specific, verifiable programming language engineered for hierarchical reasoning and FeUdal multi-agent orchestration under Spec HRL-PATENT-SPEC-2026-004-LANG.
Explore hrl-lang Repository