NVIDIA NVML library usage with NVIDIA graphic cards

The latest HWiNFO beta introduces support for Intel Nova Lake samples and fixes GPU temperature reporting and NVML driver crashes.

A mock implementation of the nvml module and driver. - rnts08/Mock-nvidia-gpu-linux

Every GPU inference container has the same problem: Kubernetes HPA can’t see the GPU. You scale on CPU and memory while your GPU sits at…

Nvidia Fan Control for linux. Contribute to ZanMax/nvidia-fan-control development by creating an account on GitHub.

══════════════════════════════════════════════════════ CELESTIUM • PRO EDITION Full GPU Optimization • Multi‑GPU Support • AI Pipeline ══════════════════════════════════════════════════════ ◆ OVERVIEW Celestium PRO is a high‑performance GPU Care System designed for AI creators, gamers, developers, and multi‑GPU workstation users. The PRO Edition unlocks all advanced features, removes trial limits, and enables full NVML, VRAM, RAM, and AI pipeline optimization. ────────────────────────────────────────────────────── ◆ SYSTEM REQUIREMENTS & EXTERNAL DEPENDENCIES Celestium PRO does NOT include NVIDIA libraries inside the software. To operate correctly, the system must have: • Latest NVIDIA GPU drivers (downloaded from nvidia.com) • CUDA runtime installed from the official NVIDIA website • Optional: PyTorch CUDA build (for advanced VRAM cleanup) Celestium does not redistribute: • CUDA Toolkit • cuDNN • PyTorch CUDA binaries • NVIDIA proprietary DLLs All core GPU components must be installed and updated online by the user. ────────────────────────────────────────────────────── ◆ GPU DETECTION & MONITORING (NVML PRO) Celestium PRO detects and monitors ALL NVIDIA GPUs in the system: • Multi‑GPU support (1–16 GPUs) • Real‑time GPU utilization (%) • Real‑time VRAM usage (used / total MiB) • GPU temperature monitoring • NVIDIA driver version detection • Per‑GPU progress bars with dynamic color coding • Full NVML engine (no fallback mode) ────────────────────────────────────────────────────── ◆ VRAM OPTIMIZATION PIPELINE (PRO) Celestium PRO includes a real VRAM optimization pipeline: • Torch CUDA cache clearing • Torch IPC cleanup • NVML VRAM refresh • Deep VRAM leak mitigation • SDXL pipeline VRAM reset • Multi‑GPU VRAM balancing ────────────────────────────────────────────────────── ◆ RAM OPTIMIZATION PIPELINE (PRO) Celestium PRO performs real system memory optimization: • Python garbage collector flush • RAM usage recalculation • Memory leak mitigation • Deep RAM cleanup for AI workloads ────────────────────────────────────────────────────── ◆ CLEANING LEVELS (FULLY UNLOCKED) All cleaning levels operate at full power: • L1 Soft Clean • L2 Medium Clean • L3 Deep Clean • L4 Extreme Clean • SDXL AI Pipeline Clean ────────────────────────────────────────────────────── ◆ AUTO CLEAN SYSTEM (PRO) Celestium PRO enables the full Auto Clean engine: • Interval‑based cleaning (1–60 minutes) • Countdown timer • Next‑clean timestamp • Automatic L1–L2 cleaning cycle • Full logging of each auto‑clean event ────────────────────────────────────────────────────── ◆ ADVANCED LOGGING SYSTEM Celestium PRO logs every operation: • Timestamped events • RAM freed (MB) • VRAM freed (MB) • Total session cleanup • Number of clean cycles • License status (PRO) • GPU state snapshots ────────────────────────────────────────────────────── ◆ SECURITY & LICENSING (ENTERPRISE LEVEL) Celestium PRO uses a hardware‑bound license system: • machine.lock (encrypted HWID) • AES‑256 encryption • PBKDF2‑HMAC‑SHA256 (1,000,000 iterations) • license.key derived from PC identity • No universal keys • No shared licenses • No trial limits ────────────────────────────────────────────────────── ◆ SUMMARY — WHAT CELESTIUM PRO CAN DO • Detect all NVIDIA GPUs (multi‑GPU) • Monitor GPU load, VRAM, temperature, driver • Optimize VRAM for AI, gaming, rendering • Optimize RAM for heavy workloads • Reset SDXL AI pipelines • Perform deep GPU cleaning (L1–L4) • Run automatic cleaning cycles • Provide detailed logs and metrics • Operate with enterprise‑grade licensing • Require official NVIDIA/CUDA updates for full functionality ══════════════════════════════════════════════════════ CELESTIUM — Security. Stability. Identity. ══════════════════════════════════════════════════════

If you run GPU workloads on Kubernetes — vLLM, Triton, training jobs, or the newer agentic inference stacks — you’ve probably hit a familiar problem: the default autoscaling path still reasons about…

NVIDIA CUDA 12.9 is now available as the newest minor feature update to NVIDIA's GPU compute stack. CUDA 12.9 adds compiler targetr support for SM 10.3 and 12.1, compiler support for "family-specific architectures", new NVML counters being exposed, and other minor feature improvements. The NVIDIA CUDA 12.9 documentation is also now more verbose in encouraging anyone still relying on Maxwell, Pascal, and Volta hardware to upgrade...

This technical datasheet describes the full capabilities of Celestium NVIDIA CARE after PRO activation. All features listed below are available exclusively in the PRO Edition and become fully unlocked once the license is validated. CELESTIUM NVIDIA CARE Professional GPU & System Memory Optimization Suite - Technical Datasheet Copyright (c) 2026 livio_dev - All Rights Reserved Secure Edition AES-256 + PBKDF2 1M | Anti-Flash NVML Architecture 1. OVERVIEW Celestium NVIDIA CARE is a high-performance Windows optimization suite designed for AI engineers, 3D creators, and GPU-intensive workloads. Provides real-time NVIDIA telemetry, intelligent VRAM/RAM cleaning, SDXL-aware maintenance, and automated system care. Built with Anti-Flash NVML architecture to eliminate console windows and subprocess flickering. 2. CORE FUNCTIONALITIES GPU Monitor * Real-time telemetry * NVML API primary, nvidia-smi fallback * Metrics: GPU name, utilization %, temperature °C, driver version, VRAM used/total MiB CPU Monitor * System load via psutil * Color-coded bars: Green <40% Yellow <70% Red <90% Magenta >90% VRAM Cleaner (L1–L4 + SDXL Mode) * L1 Soft: Python GC * L2 Medium: GC + CUDA empty cache * L3 Deep: GC + IPC collect * L4 Extreme: Full VRAM purge * SDXL Mode: Dedicated memory release for SDXL pipelines RAM Cleaner * Session tracking * MB freed before/after * psutil-based memory reclaim Auto Clean Scheduler * Interval: 1–60 minutes * Countdown timer * Next-clean timestamp * Background daemon thread Console Info Dock * Bottom-aligned info panel * Logs: timestamp, MB freed, total cleans, license status * Stores last 50 entries * Zero console flashing (CREATE_NO_WINDOW) 3. TECHNICAL ARCHITECTURE Framework: Python 3.10 + DearPyGui Monitoring: NVML primary, fallback via CREATE_NO_WINDOW (0x08000000) Loop Frequency: 150 ms Build System: Nuitka 4.2.1 standalone Flags: –disable-console –disable-plugin=anti-bloat –lto=no 4. SECURITY — AES-256 + PBKDF2 1,000,000 * AES-256: Fernet AES-128 CBC + HMAC-SHA256 * PBKDF2-1M: PBKDF2(HWID + SALT) → 32-byte DK → XXXXX-XXXXX-XXXXX-XXXXX-XXXXX * HWID: SHA256(uuid MAC + COMPUTERNAME) → 16 chars * License Types: TRIAL CLIENTE VALIDA (AES-256 + PBKDF2-1M) DEV (key.txt → CEL-LIVIO-NEG-DEV-2026) * Files: machine.lock (encrypted HWID) license.key key.txt 5. ACTIVATION WORKFLOW 6. TRIAL auto-generates machine.lock 7. Client sends machine.lock to livio_dev 8. keygen_2026.py decrypts HWID and generates license.key via PBKDF2-1M 9. Client receives license.key 10. Place both files in the application folder 11. System becomes CLIENTE VALIDA 12. SYSTEM REQUIREMENTS ## Component Requirement OS Windows 10/11 x64 GPU NVIDIA with NVML / nvidia-smi RAM 200 MB (no Torch required) Runtime None — standalone EXE Dev dearpygui, psutil, cryptography, nvidia-ml-py, nuitka Size ~45–80 MB standalone 7. PERFORMANCE & ANTI-FLASH * NVML direct API (no subprocess) * Fallback with CREATE_NO_WINDOW * 0% console flashing * <0.5% CPU usage * <50 ms query latency 8. BUILD COMMAND (Nuitka) python -m nuitka --standalone --disable-console --disable-plugin=anti-bloat \ –disable-plugin=options-nanny --windows-icon-from-ico=icons.ico \ –include-data-file=celestium_logo.png=celestium_logo.png \ –include-data-file=icons.ico=icons.ico --nofollow-import-to=torch \ –lto=no --output-dir=build nnvidia.py 9. VERSION v2026.09 — Secure Edition AES-256 + PBKDF2-1M • NVML No-Flash • Activation Ready • Multi-GPU • Console Dock • DearPyGui 1000×950 • L1–L4 + SDXL • Auto Clean 1–60 min © 2026 livio_dev — All Rights Reserved Provide machine.lock for activation.

Learn how GPU-aware autoscaling helps Kubernetes respond faster to AI workloads, reduce idle GPU costs and protect inference performance.