# High-Performance Computing

CoreWeave GPU cloud computing is a cloud platform built specifically for artificial intelligence workloads, providing flexible and efficient GPU clusters that meet the needs of enterprises in large-scale computing and storage. Its main advantages include extremely high performance, reliability, and scalability, suitable for various AI application scenarios. Through CoreWeave, users can significantly reduce cloud costs while improving service response speed, making it an ideal choice for AI innovation.

Flux is a high-performance communication overlap library developed by ByteDance, designed for tensor and expert parallelism on GPUs. Through efficient kernels and compatibility with PyTorch, it supports various parallelization strategies and is suitable for large-scale model training and inference. Flux's main advantages include high performance, ease of integration, and support for multiple NVIDIA GPU architectures. It excels in large-scale distributed training, particularly with Mixture-of-Experts (MoE) models, significantly improving computational efficiency.

3FS is a high-performance distributed file system designed for AI training and inference workloads. Leveraging modern SSDs and RDMA networks, it provides a shared storage layer, simplifying distributed application development. Its core advantages include high performance, strong consistency, and support for various workloads, significantly improving AI development and deployment efficiency. This system is suitable for large-scale AI projects, particularly excelling in data preparation, training, and inference stages.

The DeepSeek-V3/R1 inference system is a high-performance inference architecture developed by the DeepSeek team, aiming to optimize the inference efficiency of large-scale sparse models. It significantly improves GPU matrix computation efficiency and reduces latency through cross-node expert parallelism (EP) technology. The system employs a double-batch overlapping strategy and a multi-level load balancing mechanism to ensure efficient operation in large-scale distributed environments. Its main advantages include high throughput, low latency, and optimized resource utilization, making it suitable for high-performance computing and AI inference scenarios.

Thunder Compute is a GPU cloud service platform focusing on AI/ML development. Using virtualization technology, it helps users access high-performance GPU resources at a very low cost. Its main advantage is its low price, saving up to 80% of the cost compared to traditional cloud service providers. The platform supports various mainstream GPU models, such as NVIDIA Tesla T4, A100, etc., and provides 7+ Gbps network connectivity to ensure efficient data transfer. Thunder Compute aims to reduce hardware costs for AI developers and enterprises, accelerate model training and deployment, and promote the popularization and application of AI technology.

Evo 2 is an AI foundational model developed by NVIDIA, designed to decipher the genetic code of biomolecules using deep learning techniques. Developed on the NVIDIA DGX Cloud platform, it can handle large-scale genomic data, providing a powerful tool for biomedical research. A key advantage of Evo 2 is its ability to process genetic sequences up to 1 million tokens long, allowing for a more comprehensive understanding of genomic complexity. The model has broad applications in biomedicine, including disease diagnostics, drug development, and gene editing. Evo 2's development was supported by the Arc Institute and Stanford University, aiming to drive innovation and breakthroughs in biomedical research.

DeepGEMM is a CUDA library focused on high-performance FP8 matrix multiplication. Through fine-grained scaling and various optimization techniques such as Hopper TMA features, persistent thread specialization, and a fully JIT design, it significantly improves matrix computation performance. Primarily aimed at deep learning and high-performance computing, it's suitable for scenarios requiring efficient matrix operations. It supports NVIDIA Hopper architecture Tensor Cores and demonstrates superior performance across various matrix shapes. DeepGEMM boasts a concise design with a core codebase of approximately 300 lines, making it easy to learn and use while achieving performance comparable to or exceeding expert-optimized libraries. Its open-source and free nature makes it an ideal choice for researchers and developers engaged in deep learning optimization and development.

FlexHeadFA is an improved model based on FlashAttention, focusing on providing a fast and memory-efficient accurate attention mechanism. It supports flexible head dimension configuration, significantly enhancing the performance and efficiency of large language models. Key advantages include efficient GPU resource utilization, support for various head dimension configurations, and compatibility with FlashAttention-2 and FlashAttention-3. It is suitable for deep learning scenarios requiring efficient computation and memory optimization, especially excelling in handling long sequences.

FlashMLA is a high-efficiency MLA decoding kernel optimized for Hopper GPUs, specifically designed for variable-length sequence services. Developed using CUDA 12.3 and above, it supports PyTorch 2.0 and above. FlashMLA's primary advantages lie in its efficient memory access and computational performance, achieving up to 3000 GB/s memory bandwidth and 580 TFLOPS computational performance on H800 SXM5. This technology is significant for deep learning tasks requiring large-scale parallel computing and efficient memory management, especially in natural language processing and computer vision. Inspired by FlashAttention 2&3 and the cutlass project, FlashMLA aims to provide researchers and developers with a highly efficient computational tool.

NVIDIA Project DIGITS is a desktop supercomputer based on the NVIDIA GB10 Grace Blackwell superchip, designed to provide robust AI performance for AI developers. It delivers AI performance of up to 10 petaflops in an efficient, compact form factor. The product comes pre-installed with the NVIDIA AI software stack and features 128GB of memory, enabling developers to prototype, fine-tune, and infer large AI models with up to 200 billion parameters locally, and seamlessly deploy them to data centers or the cloud. The launch of Project DIGITS marks another significant milestone in NVIDIA's commitment to advancing AI development and innovation, offering developers a powerful tool to accelerate the development and deployment of AI models.

DeepSeek-V3 is a powerful Mixture-of-Experts (MoE) language model featuring a total of 671 billion parameters, activating 37 billion parameters at a time. It utilizes the Multi-head Latent Attention (MLA) and DeepSeekMoE architecture, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 introduces a novel load balancing strategy without auxiliary losses and establishes multiple-token prediction training objectives for enhanced performance. It has been pre-trained on 14.8 trillion high-quality tokens, followed by supervised fine-tuning and reinforcement learning stages to fully leverage its capabilities. Comprehensive evaluations demonstrate that DeepSeek-V3 outperforms other open-source models, achieving performance on par with leading proprietary models. Despite its outstanding performance, the complete training process of DeepSeek-V3 requires only 2.788 million H800 GPU hours, with a highly stable training environment.

FastVideo is an open-source framework designed to accelerate large video diffusion models. It offers two consistency distillation video diffusion models, FastHunyuan and FastMochi, achieving an 8x increase in inference speed. FastVideo introduces the first open video DiT distillation recipe based on PCM (Phased-Consistency-Model), supporting distillation, fine-tuning, and inference for state-of-the-art open video DiT models, including Mochi and Hunyuan. Additionally, FastVideo supports scalable training using FSDP, sequence parallelism, and selective activation checkpoints, as well as memory-efficient fine-tuning with LoRA, pre-computed latent variables, and pre-computed text embeddings. Ongoing development is highly experimental, with future plans to introduce more distillation methods, support additional models, and update the code.

Trillium TPU is Google Cloud's sixth-generation Tensor Processing Unit (TPU), specifically designed for AI workloads, offering enhanced performance and cost-effectiveness. As a key component of Google Cloud's AI Hypercomputer, it supports large-scale AI model training, fine-tuning, and inference through an integrated hardware system, open software, leading machine learning frameworks, and flexible consumption models. Trillium TPU marks a significant advancement in AI, with impressive improvements in performance, cost-efficiency, and sustainability.

DeepSeek-V2.5-1210 is an upgraded version of DeepSeek-V2.5, improving multiple capabilities including mathematics, coding, and writing inference. The model's performance in the MATH-500 benchmark has increased from 74.8% to 82.8%, while its accuracy in the LiveCodebench (08.01 - 12.01) benchmark has risen from 29.2% to 34.38%. Additionally, the new version enhances user experience in file uploads and web summarization features. The DeepSeek-V2 series (including basics and chat capabilities) supports commercial use.

Rain AI is focused on developing energy-efficient artificial intelligence hardware. In the context of increasing energy consumption, Rain AI's products reduce energy consumption through optimized hardware design while maintaining high performance, which is crucial for data centers and enterprises that require significant computing resources. Key advantages of the products include high energy efficiency, high performance, and environmentally friendly design. Background information indicates that the company is committed to promoting the sustainable development of AI technology and reducing environmental impacts through technological innovation. Pricing and positioning specifics are not yet defined, but it can be inferred that the target market consists of enterprises that require high-performance computing and demand energy efficiency.

The tiiuae/falcon-mamba-7b is a high-performance causal language model developed by TII UAE, based on the Mamba architecture and specifically designed for generation tasks. The model has demonstrated outstanding performance across multiple benchmarks and is capable of running on various hardware configurations, supporting multiple precision settings to accommodate different performance and resource needs. It was trained utilizing advanced 3D parallel strategies and ZeRO optimization techniques, enabling efficient training on large GPU clusters.

The AMD Instinct MI325X accelerator is based on the AMD CDNA 3 architecture and is specifically designed for AI tasks, including foundational model training, fine-tuning, and inference, delivering exceptional performance and efficiency. These products enable AMD's customers and partners to create high-performance and optimized AI solutions at the system, rack, and data center levels. The AMD Instinct MI325X offers industry-leading memory capacity and bandwidth, supporting 256GB HBM3E with 6.0TB/s, representing 1.8 times the capacity and 1.3 times the bandwidth of the H200, resulting in increased FP16 and FP8 computing performance.

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.

SiFive is a leader in RISC-V architecture, offering high-performance, efficient computing solutions that are suitable for automotive, AI, data centers, and other applications. Its products are characterized by superior performance and efficiency, alongside support from a global community, driving the development and application of RISC-V technology.

Groq is a company that provides high-performance AI chips and cloud services, focusing on ultra-low latency inference for AI models. Since the launch of its product GroqCloud? in February 2024, it has been utilized by over 467,000 developers. The AI chip technology of Groq is supported by Yann LeCun, Chief AI Scientist at Meta, and has secured $640 million in funding led by BlackRock, resulting in a company valuation of $2.8 billion. Groq's technological advantage lies in its seamless migration capability from other providers with just three lines of code modification, and it is compatible with OpenAI's endpoints. Groq's AI chips aim to challenge Nvidia's dominance in the AI chip market by offering faster and more efficient AI inference solutions for developers and businesses.

The Qwen2.5 series language models consist of a range of open-source decoder-only dense models with parameter sizes ranging from 0.5B to 72B, designed to meet various product requirements for model scale. These models excel in natural language understanding, code generation, mathematical reasoning, and many other fields, making them particularly suitable for applications that require high-performance language processing. The release of the Qwen2.5 series models marks a significant advancement in the realm of large language models, providing powerful tools for developers and researchers.

Azure Quantum is a quantum computing platform launched by Microsoft, aimed at accelerating discoveries in scientific research and materials science through cutting-edge quantum computing technology. By integrating artificial intelligence, high-performance computing, and quantum computing, it offers a comprehensive set of tools and resources to assist researchers and developers in achieving breakthroughs in the quantum domain. The vision of Azure Quantum is to accelerate 250 years of scientific progress into the next 25 years, addressing the world’s most challenging problems with quantum supercomputers.

Graphcore is a company specializing in AI hardware accelerators. Its products primarily serve the AI field requiring high-performance computing. Graphcore's IPU (Intelligence Processing Unit) technology provides powerful computational support for machine learning, deep learning, and other AI applications. The company's product line includes cloud-based IPUs, data center IPUs, and Bow IPU processors, all optimized by Poplar? Software to significantly enhance the training and inference speeds of AI models. Graphcore's products and technologies find applications across various industries, including finance, biotechnology, and research, helping enterprises and research institutions accelerate AI project experimentation and improve efficiency.

Skywork-MoE is a 146-billion parameter high-performance Mixture of Experts (MoE) model, featuring 16 experts and 2.2 billion activation parameters. This model is initialized from the dense checkpoint of the Skywork-13B model. Two innovative techniques are introduced: gated logic normalization, enhancing expert diversity; and adaptive auxiliary loss coefficient, allowing layer-specific auxiliary loss coefficient adjustment. Skywork-MoE demonstrates comparable or superior performance to models with more parameters or activation parameters across various popular benchmark tests, such as C-Eval, MMLU, CMMLU, GSM8K, MATH, and HumanEval.

Crusoe offers a scalable and climate-aligned digital infrastructure optimized for high-performance computing and artificial intelligence. Our innovative approach minimizes greenhouse gas emissions by leveraging wasted, isolated, or clean energy, supporting the energy transition, and maximizing resource efficiency.

The Llama-3 70B Gradient 524K Adapter is an extension of the Llama-3 70B model, developed by the Gradient AI Team. It is designed to extend the model's context length to over 524K through LoRA technology, thereby enhancing the model's performance in handling long text data. The model employs advanced training technologies, including NTK-aware interpolation and the RingAttention library, to efficiently train within high-performance computing clusters.

Solidus Ai Tech is an innovative technology company providing Artificial Intelligence as a Service (AIAAS), Blockchain as a Service (BAAS), High-Performance Computing (HPC), and an Artificial Intelligence marketplace. Our core token, AITECH, drives the development of all these cutting-edge technologies. Through our platform, users can easily access the latest AI technologies to solve complex problems, improve efficiency, and achieve their business goals.

StableCode is the first AI product for programming released by Stability AI. It utilizes three distinct models to enhance developer productivity. The foundation model was trained on BigCode's stack-dataset (v1.2) and further fine-tuned for popular programming languages such as Python, Go, Java, Javascript, C, markdown, and C++. We trained a total of 560B code tokens on a high-performance computing cluster. Subsequently, the foundation model was fine-tuned using approximately 120,000 code instruction/response pairs to address complex programming tasks. StableCode serves as an ideal foundation for learning programming. The long-text environment window model provides users with single-line and multi-line auto-completion suggestions. This model can process more code than previous open-source models (2-4 times more, with a context window of 16,000 tokens), allowing users to view or edit code equivalent to five average-sized Python files simultaneously. This makes it an ideal learning tool for beginners who are ready to take on greater challenges.