# Inference

Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model based on Llama-3.1-405B-Instruct, which has undergone multi-stage post-training to enhance reasoning and chat capabilities. This model supports context lengths up to 128K, offering a good balance between accuracy and efficiency. Suitable for commercial use, it aims to provide developers with powerful AI assistant functionality.

Huginn-0125 is a latent variable recurrent deep model developed by the Tom Goldstein Lab at the University of Maryland, College Park. This model, trained on 800 billion tokens, showcases exceptional performance in inference and code generation with its 3.5 billion parameters. Its core feature is the dynamic adjustment of computation at test time through a recurrent deep structure, allowing for flexible adaptation of computation steps based on task requirements, thereby optimizing resource utilization while maintaining performance. The model is available on the open-source Hugging Face platform, supporting community sharing and collaboration, allowing users to download, use, and further develop it freely. Its open-source nature and flexible architecture make it a vital tool in research and development, particularly in resource-constrained situations or where high-performance inference is necessary.

DeepClaude is a powerful AI tool designed to combine the inference capabilities of DeepSeek R1 with Claude's creativity and code generation abilities. It provides services through a unified API and chat interface, utilizing a high-performance streaming API (written in Rust) for instant responses, while supporting end-to-end encryption and local API key management to ensure user data privacy and security. The product is fully open-source, allowing users to freely contribute, modify, and deploy. Its main advantages include zero latency responses, high configurability, and support for bring-your-own-key (BYOK), providing developers with exceptional flexibility and control. DeepClaude targets developers and enterprises needing efficient code generation and AI inference capabilities, currently in a free trial phase with potential future usage-based pricing.

Gemini Flash Thinking is the latest AI model launched by Google DeepMind, specifically designed for complex tasks. It can demonstrate the inference process, helping users better understand the decision-making logic of the model. This model excels in mathematics and science domains, supporting long-text analysis and code execution capabilities. It aims to provide developers with powerful tools to advance the application of artificial intelligence in complex tasks.

DeepSeek-R1-Distill-Qwen-14B is a distilled model developed by the DeepSeek team based on Qwen-14B, focusing on inference and text generation tasks. This model significantly enhances inference capability and generation quality through large-scale reinforcement learning and data distillation techniques while reducing computational resource requirements. Its main advantages include high performance, low resource consumption, and broad applicability, making it suitable for scenarios requiring efficient inference and text generation.

Tost AI is a free, non-profit, open-source service that provides inference for the latest AI research papers, utilizing a non-profit GPU cluster. Tost AI does not store any inference data; all data expires within 12 hours. Additionally, it offers the option to send data to a Discord channel. Each account receives 100 free wallet credits daily. If you wish to earn 1100 daily wallet credits, you can subscribe as a GitHub sponsor or through Patreon. All profits from Tost AI are directed to the first author of the papers, funded by corporate and individual sponsors.

vLLM is a fast, easy-to-use, and efficient library for large language model (LLM) inference and service provision. By leveraging the latest service throughput technologies, efficient memory management, continuous batch processing requests, CUDA/HIP graph fast model execution, quantization techniques, and optimized CUDA kernels, it provides high-performance inference services. vLLM seamlessly integrates with popular HuggingFace models, supports various decoding algorithms including parallel sampling and beam search, supports tensor parallelism for distributed inference, supports streaming output, and is compatible with OpenAI API servers. Moreover, vLLM supports both NVIDIA and AMD GPUs, as well as experimental prefix caching and multi-lora support.

Fireworks collaborates with world-leading generative AI researchers to deliver the best models at the fastest pace. Featuring carefully curated and optimized models from Fireworks, alongside enterprise-grade throughput and expert technical support. Position itself as the fastest and most reliable AI platform.

OpenDiT is an open-source project providing a high-performance implementation of Diffusion Transformer (DiT) based on Colossal-AI. It is designed to enhance the training and inference efficiency of DiT applications, including text-to-video and text-to-image generation. OpenDiT achieves performance improvements through the following technologies: * GPU acceleration up to 80% and 50% memory reduction; * Core optimizations including FlashAttention, Fused AdaLN, and Fused layernorm; * Mixed parallelism methods such as ZeRO, Gemini, and DDP, along with model sharding for ema models to further reduce memory costs; * FastSeq: A novel sequence parallelism method particularly suitable for workloads like DiT, where activations are large but parameters are small. Single-node sequence parallelism can save up to 48% in communication costs and break through the memory limit of a single GPU, reducing overall training and inference time; * Significant performance improvements can be achieved with minimal code modifications; * Users do not need to understand the implementation details of distributed training; * Complete text-to-image and text-to-video generation workflows; * Researchers and engineers can easily use and adapt our workflows to real-world applications without modifying the parallelism part; * Training on ImageNet for text-to-image generation and releasing checkpoints.

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.

Flash-Decoding is a technique for long-context inference that can significantly accelerate the attention mechanism during inference, leading to an 8x improvement in generation speed. This technique achieves faster inference speed by parallelly loading keys and values and then rescaling and combining the results to maintain the correct attention output. Flash-Decoding is suitable for large language models and can handle long contexts such as long documents, long conversations, or entire codebases. Flash-Decoding is available in the FlashAttention package and xFormers, which can automatically select between Flash-Decoding and FlashAttention methods. It can also utilize the efficient Triton kernel.

Stability AI Generation Models is an open-source generation model library providing functions for training, inference, and application of various generation models. The library supports training diverse generation models, including PyTorch Lightning-based training, offering rich configuration options and a modular design. Users can leverage this library to train generation models and perform inference and application using the provided models. The library also includes example training configurations and data processing functionalities, facilitating quick start and customization for users.