Best 8 AI model inference training Tools of 2025

Intel Core Ultra Desktop Processors
Intel Core Ultra Desktop Processors
The Intel? Core? Ultra 200 series desktop processors are the first AI PC processors designed for the desktop platform, delivering exceptional gaming experiences and industry-leading computing performance while significantly reducing power consumption. These processors feature up to 8 next-generation performance cores (P-cores) and up to 16 next-generation efficiency cores (E-cores), resulting in up to a 14% performance improvement in multi-threaded workloads compared to the previous generation. They are also the first desktop processors equipped with a neural processing unit (NPU) for enthusiasts, and include built-in Xe GPU technology, supporting advanced media features.
AI model inference training
45.0K
Intel Gaudi 3 AI Accelerator
Intel Gaudi 3 AI Accelerator
The Intel? Gaudi? 3 AI Accelerator is a high-performance artificial intelligence accelerator launched by Intel, built on the efficient Intel? Gaudi? platform. It boasts outstanding MLPerf benchmark performance and is designed to handle demanding training and inference tasks. The accelerator supports AI applications such as large language models, multimodal models, and enterprise RAG in data centers or the cloud, operating on your existing Ethernet infrastructure. Whether you need a single accelerator or thousands, Intel Gaudi 3 can play a crucial role in your AI success.
AI model inference training
50.5K
aimo-progress-prize
Aimo Progress Prize
This GitHub repository contains training and inference code to replicate our winning solution in the AI Mathematics Olympic (AIMO) Progress Prize 1. Our solution consists of four main parts: a recipe for fine-tuning DeepSeekMath-Base 7B for use in solving math problems using Tool Integrated Reasoning (TIR); two high-quality datasets of about 10 million math questions and solutions; an algorithm for generating solution candidates with coding execution feedback (SC-TIR); and four carefully selected validation sets from AMC, AIME, and MATH to guide model selection and avoid overfitting the public leaderboard.
AI model inference training
59.1K
HippoRAG
Hipporag
HippoRAG is a novel Retriever-Augmented Generation (RAG) framework inspired by human long-term memory, enabling Large Language Models (LLMs) to continuously integrate knowledge across external documents. Experiments demonstrate that HippoRAG can provide the capabilities of RAG systems, typically requiring expensive and high-latency iterative LLM pipelines, at a lower computational cost.
AI model inference training
59.6K
T3
T3
Large language models increasingly rely on distributed techniques for training and inference. These techniques necessitate communication between devices, and as the number of devices increases, this can degrade scaling efficiency. While some distributed techniques can overlap communication to hide independent computation, techniques like tensor parallelism (TP) inherently serialize communication with model execution. One way to hide this serialized communication is to interweave it with producer operations (data generation) in a fine-grained manner. However, implementing this fine-grained communication and computation interleaving in software can be challenging. Furthermore, like any concurrent execution, it requires sharing computational and memory resources between computation and communication, leading to resource contention and decreased overlap efficiency. To overcome these challenges, we propose T3, which uses hardware-software co-design to transparently overlap serialized communication while minimizing resource contention with computation. T3, through simple configuration of producer output address spaces, transparently fuses producer operations and subsequent communication, requiring minimal software changes. At the hardware level, T3 incorporates lightweight tracking and triggering mechanisms to orchestrate producer computation and communication. It further leverages enhanced compute memory for computation related to communication. Consequently, T3 reduces resource contention and effectively overlaps serialized communication with computation. For important Transformer models like T-NLG, T3 achieves a geometric mean speedup of 30% (up to 47%) for communication-intensive sublayers and a geometric mean reduction of 22% (up to 36%) in data movement. Furthermore, T3's benefits persist as models scale: achieving a geometric mean speedup of 29% for sublayers in the 500B parameter sim model, PALM, and MT-NLG.
AI model inference training
45.0K
Zero Bubble Pipeline Parallelism
Zero Bubble Pipeline Parallelism
Zero Bubble Pipeline Parallelism is a crucial component of large-scale distributed training, and its efficiency is affected by pipeline bubbles. We introduce a scheduling strategy that successfully achieves zero pipeline bubbles under synchronous training semantics. The core idea behind this improvement is to divide backward calculation into two parts: one part calculates the gradients of the input, and the other part calculates the gradients of the parameters. Based on this idea, we manually designed novel pipeline scheduling, which significantly outperforms benchmark methods. We further developed an algorithm that automatically finds the optimal scheduling based on specific model configuration and memory constraints. Furthermore, to truly achieve zero bubbles, we introduce a novel technique that bypasses synchronization during optimizer steps. Experimental evaluation demonstrates that our method achieves up to 23% higher throughput than the 1F1B schedule under similar memory constraints. This number can further increase to 31% when memory constraints are relaxed. We believe our results mark an important step towards realizing the potential of pipeline parallelism.
AI model inference training
51.9K
ReFT
Reft
ReFT is a simple yet effective method for enhancing the reasoning capabilities of large language models (LLMs). It first preheats the model through supervised fine-tuning (SFT), and then further fine-tunes the model using online reinforcement learning, specifically the PPO algorithm presented in this paper. ReFT significantly outperforms SFT by automatically sampling a large number of reasoning paths for a given problem and naturally deriving rewards from the true answers. ReFT's performance can be further improved by combining reasoning strategies (such as majority voting and re-ranking). It's noteworthy that ReFT achieves improvements by learning from the same training questions as SFT, without relying on additional or enhanced training questions. This demonstrates ReFT's stronger generalization ability.
AI model inference training
51.6K
Efficient LLM
Efficient LLM
This is an efficient LLM inference solution implemented on Intel GPUs. By simplifying the LLM decoder layer, utilizing segment KV caching strategies, and implementing a custom Scaled-Dot-Product-Attention kernel, this solution achieves up to 7x lower token latency and 27x higher throughput on Intel GPUs compared to the standard HuggingFace implementation. For detailed features, advantages, pricing, and positioning information, please refer to the official website.
AI model inference training
45.5K
Featured AI Tools
Flow AI
Flow AI
Flow is an AI-driven movie-making tool designed for creators, utilizing Google DeepMind's advanced models to allow users to easily create excellent movie clips, scenes, and stories. The tool provides a seamless creative experience, supporting user-defined assets or generating content within Flow. In terms of pricing, the Google AI Pro and Google AI Ultra plans offer different functionalities suitable for various user needs.
Video Production
43.1K
NoCode
Nocode
NoCode is a platform that requires no programming experience, allowing users to quickly generate applications by describing their ideas in natural language, aiming to lower development barriers so more people can realize their ideas. The platform provides real-time previews and one-click deployment features, making it very suitable for non-technical users to turn their ideas into reality.
Development Platform
46.1K
ListenHub
Listenhub
ListenHub is a lightweight AI podcast generation tool that supports both Chinese and English. Based on cutting-edge AI technology, it can quickly generate podcast content of interest to users. Its main advantages include natural dialogue and ultra-realistic voice effects, allowing users to enjoy high-quality auditory experiences anytime and anywhere. ListenHub not only improves the speed of content generation but also offers compatibility with mobile devices, making it convenient for users to use in different settings. The product is positioned as an efficient information acquisition tool, suitable for the needs of a wide range of listeners.
AI
43.6K
MiniMax Agent
Minimax Agent
MiniMax Agent is an intelligent AI companion that adopts the latest multimodal technology. The MCP multi-agent collaboration enables AI teams to efficiently solve complex problems. It provides features such as instant answers, visual analysis, and voice interaction, which can increase productivity by 10 times.
Multimodal technology
45.3K
Chinese Picks
Tencent Hunyuan Image 2.0
Tencent Hunyuan Image 2.0
Tencent Hunyuan Image 2.0 is Tencent's latest released AI image generation model, significantly improving generation speed and image quality. With a super-high compression ratio codec and new diffusion architecture, image generation speed can reach milliseconds, avoiding the waiting time of traditional generation. At the same time, the model improves the realism and detail representation of images through the combination of reinforcement learning algorithms and human aesthetic knowledge, suitable for professional users such as designers and creators.
Image Generation
44.2K
OpenMemory MCP
Openmemory MCP
OpenMemory is an open-source personal memory layer that provides private, portable memory management for large language models (LLMs). It ensures users have full control over their data, maintaining its security when building AI applications. This project supports Docker, Python, and Node.js, making it suitable for developers seeking personalized AI experiences. OpenMemory is particularly suited for users who wish to use AI without revealing personal information.
open source
43.9K
FastVLM
Fastvlm
FastVLM is an efficient visual encoding model designed specifically for visual language models. It uses the innovative FastViTHD hybrid visual encoder to reduce the time required for encoding high-resolution images and the number of output tokens, resulting in excellent performance in both speed and accuracy. FastVLM is primarily positioned to provide developers with powerful visual language processing capabilities, applicable to various scenarios, particularly performing excellently on mobile devices that require rapid response.
Image Processing
42.0K
Chinese Picks
LiblibAI
Liblibai
LiblibAI is a leading Chinese AI creative platform offering powerful AI creative tools to help creators bring their imagination to life. The platform provides a vast library of free AI creative models, allowing users to search and utilize these models for image, text, and audio creations. Users can also train their own AI models on the platform. Focused on the diverse needs of creators, LiblibAI is committed to creating inclusive conditions and serving the creative industry, ensuring that everyone can enjoy the joy of creation.
AI Model
6.9M
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