Search Results for "gpu max performance"
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Showing 13 open source projects for "gpu max performance"
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SwiftShader
SwiftShader is a high-performance CPU-based implementation
SwiftShader is Google’s high-performance CPU-based implementation of the Vulkan 1.3 graphics API, designed to provide a hardware-independent rendering solution for 3D graphics. Unlike traditional GPU drivers, SwiftShader executes graphics commands entirely on the CPU, making it ideal for environments where dedicated graphics hardware is unavailable or unsuitable. It acts as a drop-in replacement for Vulkan drivers, allowing existing applications to run seamlessly by redirecting API calls through its software-based rendering engine. ... -
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RLax
Library of JAX-based building blocks for reinforcement learning agents
...It supports both on-policy and off-policy learning, as well as value-based, policy-based, and model-based approaches. RLax is fully JIT-compilable with JAX, enabling high-performance execution across CPU, GPU, and TPU backends. The library implements tools for Bellman equations, return distributions, general value functions, and policy optimization in both continuous and discrete action spaces. It integrates seamlessly with DeepMind’s Haiku (for neural network definition) and Optax (for optimization), making it a key component in modular RL pipelines. -
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LitServe
Minimal Python framework for scalable AI inference servers fast
LitServe is a minimal Python framework designed for building custom AI inference servers with full control over how models are executed and served. It allows developers to define their own inference logic, making it suitable for complex systems such as multi-model pipelines, agents, and retrieval-augmented generation workflows. Unlike traditional serving tools that enforce rigid abstractions, LitServe focuses on flexibility by letting users control request handling, batching strategies, and... -
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Diplomacy Cicero
Code for Cicero, an AI agent that plays the game of Diplomacy
...It supports two variants: Cicero (which handles full “press” negotiation) and Diplodocus (a variant focused on no-press diplomacy) as described in the README. The codebase is implemented primarily in Python with performance-critical components in C++ (via pybind11 bindings) and is configured to run in a high‐GPU cluster environment. Configuration is managed via protobuf files to define tasks such as self-play, benchmark agent comparisons, and RL training. The project is now archived and read-only, reflecting that it is no longer actively developed but remains publicly available for research use. -
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Tracking Any Point (TAP)
DeepMind model for tracking arbitrary points across videos & robotics
TAPNet is the official Google DeepMind repository for Tracking Any Point (TAP), bundling datasets, models, benchmarks, and demos for precise point tracking in videos. The project includes the TAP-Vid and TAPVid-3D benchmarks, which evaluate long-range tracking of arbitrary points in 2D and 3D across diverse real and synthetic videos. Its flagship models—TAPIR, BootsTAPIR, and the latest TAPNext—use matching plus temporal refinement or next-token style propagation to achieve state-of-the-art... -
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NebiOS Alternative Mirror (Official)
A unique Ubuntu-based Linux OS with its own desktop and ecosystem.
...Main site: https://nebios.org 📌 This mirror page is updated less frequently than the main site. For the latest information, visit https://nebios.org ⚠️ Don't download from unknown sources. ⚠️ Virtual machines: NebiOS X requires GPU acceleration. Most hypervisors (VirtualBox, VMware, GNOME Boxes) will show degraded performance. QEMU with 3D acceleration enabled gives the best VM experience. See FAQ for details. System requirements and pre-installation info: https://nebios.org/download (select your version) Full FAQ: https://nebios.org/support/faq -
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VirtualGL
3D Without Boundaries
VirtualGL redirects 3D commands from a Unix/Linux OpenGL application onto a server-side GPU and converts the rendered 3D images into a video stream with which remote clients can interact to view and control the 3D application in real time. -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Mi A1: LuciferKernel
Linux 4.9 Kernel for Xiaomi Mi A1 aka Tissot
Join official telegram group: https://t.me/luciferkernel Kernel stage Changed to Beta. Features: →Added F2FS Rapid Gc. → CPU OC to 3.1Mhz (OC Build Only) → GPU OC to 850Mhz (OC Build Only) → Reduced GPU idle time to 64ms → Added DTS-Eagle. → CPU undervolted upto 10% → Improved thermals. → Added Sound tweaks. → Added Boeffla Wakelocker → Added State Notifier → Added Adreno Idler → Added Adreno Boost → Added dynamic SchedTune boost. → Added Klapse → Added KCAL → Added BFQ I/O scheduler →Upstreamed to 4.9.232 →Added gcam7+ support (experimental) thanks to @mtpiplod →Fixed certain errors →Built with Proton Clang 12 For Non-OC except Overclocked CPU , GPU and some performance schedulers everything is same -
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macOS Simple KVM
Tools to set up a quick macOS VM in QEMU, accelerated by KVM
...The repository includes tools for preparing installation media, configuring virtual hardware, and managing VM launch scripts. By using KVM acceleration, the virtual machine runs with near-native performance, making it useful for testing, development, or personal experimentation. The project also supports GPU passthrough and other advanced configurations for users who want a more optimized macOS VM environment. While primarily intended for educational and testing purposes, it demonstrates how macOS can be virtualized outside of Apple hardware. -
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RpiOptimisation
Performance optimization tool for Raspberry Pi.
Support : [Raspberry Pi 0-1-2-3] [EN] Performance optimization tool for Raspberry Pi (CPU,GPU,SD card read and write speed) by model. [FR] Outil d'optimisation des performances pour Raspberry Pi (CPU, GPU, vitesse de lecture et d'écriture de la carte SD) par modèle. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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broodKernel
Latest i9001 stock kernel, heavily tweaked!
broodKernel is a kernel that is always based on the latest i9001 kernel, the big difference between an other custom kernel is that broodKernel keeps the original zImage. This means that all stock features are available, in order to tweak it I added a lot of tweaks to the kernels ramdisk. next to the ramdisk tweaks there are 2 init.d scripts which allow you to manage broodKernel the way you want. Visit the github here: https://github.com/broodplank/broodKernel
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