Top BaseRT Alternatives

LMCache represents a cutting-edge open-source Knowledge Delivery Network (KDN) that acts as a caching layer specifically designed for large language models, significantly boosting inference speeds by enabling the reuse of key-value (KV) caches during repeated or overlapping computations. This innovative system streamlines prompt caching, allowing LLMs to "prefill" recurring text only once, which can then be reused in multiple locations across different serving instances. By adopting this approach, the time taken to produce the first token is greatly reduced, leading to conservation of GPU cycles and enhanced throughput, especially beneficial in scenarios like multi-round question answering and retrieval-augmented generation. Furthermore, LMCache includes capabilities such as KV cache offloading, which permits the transfer of caches from GPU to CPU or disk, facilitates cache sharing among various instances, and supports disaggregated prefill for improved resource efficiency. It integrates smoothly with inference engines like vLLM and TGI, while also accommodating compressed storage formats, merging techniques for cache optimization, and a wide range of backend storage solutions. Overall, the architecture of LMCache is meticulously designed to maximize both performance and efficiency in the realm of language model inference applications, ultimately positioning it as a valuable tool for developers and researchers alike. In a landscape where the demand for rapid and efficient language processing continues to grow, LMCache's capabilities will likely play a crucial role in advancing the field.

Muse Spark Open is a large-scale open-weight artificial intelligence model developed by Meta for multimodal understanding, software engineering, autonomous agents, and long-horizon reasoning. Released in October 2026, the model provides downloadable weights that allow developers to deploy and customize it outside Meta's hosted AI services. It uses a mixture-of-experts architecture containing 816 billion total parameters, with approximately 42 billion parameters activated during inference. Its one-million-token context window supports processing extensive documents, large software repositories, and lengthy sequences of instructions and interactions. The model is designed for complex coding workflows involving source code analysis, debugging, feature implementation, application development, and software testing. Its multimodal capabilities enable AI agents to interpret visual information and combine that understanding with reasoning and automated computer interactions. Meta demonstrated the model performing local software development tasks and controlling DaVinci Resolve to analyze video footage and prepare edited content. Muse Spark Open supports on-device deployment using two Apple M5 Ultra Mac Studio computers running the MLX framework, with additional compatibility across AMD, Intel, and NVIDIA hardware. Supported inference frameworks include SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, llama.cpp, and Unsloth, with optimized GPU kernels for improved processing performance. Developers can customize the model using PyTorch and TorchTitan to adapt its behavior to specialized tasks, organizational requirements, and application workflows. Muse Spark Open is distributed under the Muse Model License with limited commercial terms and is available through Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, Microsoft Foundry Models, and several hosted API providers.

EXAONE Deep is a suite of sophisticated language models developed by LG AI Research, featuring configurations of 2.4 billion, 7.8 billion, and 32 billion parameters. These models are particularly adept at tackling a range of reasoning tasks, excelling in domains like mathematics and programming evaluations. Notably, the 2.4B variant stands out among its peers of comparable size, while the 7.8B model surpasses both open-weight counterparts and the proprietary model OpenAI o1-mini. Additionally, the 32B variant competes strongly with leading open-weight models in the industry. The accompanying repository not only provides comprehensive documentation, including performance metrics and quick-start guides for utilizing EXAONE Deep models with the Transformers library, but also offers in-depth explanations of quantized EXAONE Deep weights structured in AWQ and GGUF formats. Users will also find instructions on how to operate these models locally using tools like llama.cpp and Ollama, thereby broadening their understanding of the EXAONE Deep models' potential and ensuring easier access to their powerful capabilities. This resource aims to empower users by facilitating a deeper engagement with the advanced functionalities of the models.

Lucebox is an all-in-one computer tailored for running local AI models and agents with optimal efficiency. Its unique enclosure contains a Ryzen AI MAX+ 395 processor paired with an impressive 128GB of unified LPDDR5X memory and an RTX 3090 graphics card, which collaborate seamlessly through a meticulously tuned open-source inference engine designed specifically for this hardware setup. The architecture's design plays a crucial role in delivering outstanding performance. The abundant 128GB of unified memory efficiently accommodates large models, while the high-bandwidth VRAM of the RTX 3090 acts as a swift access layer. Utilizing advanced techniques such as speculative decoding (DFlash) and speculative prefill (PFlash), these two memory systems are interconnected, resulting in inference speeds that can be as much as ten times quicker than llama.cpp operating on identical hardware. This remarkable capability allows it to surpass competitors like the Mac Studio and DGX Spark, all while maintaining a far more budget-friendly price point. In addition, the combination of cutting-edge hardware and software enhancements firmly establishes Lucebox as a prominent contender in the realm of local AI computing, appealing to developers and enthusiasts alike.

oMLX is a dedicated MLX server optimized for macOS, which significantly boosts the speed and efficiency of local AI tasks on Apple Silicon hardware. It specifically addresses the needs of coding agents by employing paged SSD KV caching, allowing cache blocks to be retained on disk; thus, previously accessed prefixes can be swiftly retrieved across various requests and even after server restarts, negating the need for recalculation from the ground up. Consequently, the duration required to produce the first token in extensive contexts can drop dramatically, from a span of 30 to 90 seconds down to under five seconds following the initial interaction. The server skillfully handles multiple requests simultaneously through a constant batching approach using mlx-lm’s BatchGenerator, which improves overall generation throughput by preventing requests from queuing behind a single task. oMLX can serve a diverse array of models concurrently, including LLMs, vision-language models, embedding models, and rerankers, while efficiently managing memory limitations through LRU eviction. Additionally, it supports any MLX-format model available from Hugging Face, including Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and has the capability to work with models stored in the regular Hugging Face cache, directories linked to LM Studio, or any custom storage solutions, thus providing a seamless experience for users. This adaptability in model integration not only enhances the functionality of oMLX but also significantly benefits developers and researchers, making it a practical tool in various AI applications. Overall, oMLX stands out as a robust solution for maximizing the potential of AI on macOS systems.

DeepInfer is an open-source framework tailored for running DeepSeek language models on Linux systems equipped with compatible NVIDIA or Huawei Ascend accelerators. It boasts a range of features intended to streamline offline inference and enhance model performance, such as optimized kernels, distributed parallelism, asynchronous batch scheduling, speculative decoding, and the creation of structured outputs in formats including JSON Schema, regular expressions, and EBNF. The documentation for the project includes support details for DeepSeek-V4.1-Flash, along with thorough instructions for building, configuring, and utilizing various examples and evaluation tools. Targeted at developers and infrastructure teams, it aids in the development or evaluation of self-managed inference deployments. Furthermore, the repository emphasizes that for production environments, additional components like KV-cache, prefill/decode disaggregation, and load-balancing mechanisms may be necessary, making users conscious of possible extra requirements as they implement the framework. In summary, DeepInfer not only optimizes the deployment of language models across diverse applications but also caters to both essential and sophisticated operational needs, making it a versatile tool for developers. This framework ultimately encourages innovation in language processing and model deployment strategies.

vLLM is an innovative library specifically designed for the efficient inference and deployment of Large Language Models (LLMs). Originally developed at UC Berkeley's Sky Computing Lab, it has evolved into a collaborative project that benefits from input by both academia and industry. The library stands out for its remarkable serving throughput, achieved through its unique PagedAttention mechanism, which adeptly manages attention key and value memory. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, leveraging technologies such as FlashAttention and FlashInfer to enhance model execution speed significantly. In addition, vLLM accommodates several quantization techniques, including GPTQ, AWQ, INT4, INT8, and FP8, while also featuring speculative decoding capabilities. Users can effortlessly integrate vLLM with popular models from Hugging Face and take advantage of a diverse array of decoding algorithms, including parallel sampling and beam search. It is also engineered to work seamlessly across various hardware platforms, including NVIDIA GPUs, AMD CPUs and GPUs, and Intel CPUs, which assures developers of its flexibility and accessibility. This extensive hardware compatibility solidifies vLLM as a robust option for anyone aiming to implement LLMs efficiently in a variety of settings, further enhancing its appeal and usability in the field of machine learning.

LFM2 is a cutting-edge series of on-device foundation models specifically engineered to deliver an exceptionally fast generative-AI experience across a wide range of devices. It employs an innovative hybrid architecture that enables decoding and pre-filling speeds up to twice as fast as competing models, while also improving training efficiency by as much as threefold compared to earlier versions. Striking a perfect balance between quality, latency, and memory use, these models are ideally suited for embedded system applications, allowing for real-time, on-device AI capabilities in smartphones, laptops, vehicles, wearables, and many other platforms. This results in millisecond-level inference, enhanced device longevity, and complete data sovereignty for users. Available in three configurations with 0.35 billion, 0.7 billion, and 1.2 billion parameters, LFM2 demonstrates superior benchmark results compared to similarly sized models, excelling in knowledge recall, mathematical problem-solving, adherence to multilingual instructions, and conversational dialogue evaluations. With such impressive capabilities, LFM2 not only elevates the user experience but also establishes a new benchmark for on-device AI performance, paving the way for future advancements in the field.

LTM-2-mini is designed to manage a context of 100 million tokens, which is roughly equivalent to about 10 million lines of code or approximately 750 full-length novels. This model utilizes a sequence-dimension algorithm that proves to be around 1000 times more economical per decoded token compared to the attention mechanism employed by Llama 3.1 405B when operating within the same 100 million token context window. Additionally, the difference in memory requirements is even more pronounced; running Llama 3.1 405B with a 100 million token context requires an impressive 638 H100 GPUs per user just to sustain a single 100 million token key-value cache. In stark contrast, LTM-2-mini only needs a tiny fraction of the high-bandwidth memory available in one H100 GPU for the equivalent context, showcasing its remarkable efficiency. This significant advantage positions LTM-2-mini as an attractive choice for applications that require extensive context processing while minimizing resource usage. Moreover, the ability to efficiently handle such large contexts opens the door for innovative applications across various fields.

Distil Labs improves the efficiency of AI by replacing expensive calls to sophisticated models with custom small language models tailored for particular tasks, all while maintaining high-quality benchmarks. They analyze real production traffic and collect traces from current large language model requests to create a comprehensive evaluation set that reveals genuine workload characteristics. Subsequently, the company develops and validates synthetic training data, ensuring that its distribution aligns with the desired workload, and employs supervised fine-tuning in conjunction with reinforcement learning techniques. Once the model is quantized, an optimized endpoint is created to support its deployment. The results are rigorously compared to the existing model in terms of accuracy, latency, and overall efficiency, offering teams the information necessary to decide when to scale up traffic. Furthermore, the OpenAI-compatible endpoint incorporates a specialized small language model, prompt optimization, efficient caching, and enhanced serving tailored for specific applications, thereby guaranteeing optimal performance. By adopting this thorough methodology, organizations are empowered to fully leverage the capabilities of their AI solutions, ultimately driving greater innovation and effectiveness within their operations.

Wafer is transforming the landscape of enterprise AI by providing the fastest open-source LLMs, tailored for both serverless and dedicated inference specifically aimed at production workloads. Their serverless inference solution allows teams to leverage premium open models without the hassle of managing infrastructure or deployment issues, offering quick APIs like GLM-5.2-Fast, which minimizes latency through EAGLE speculative decoding and guarantees throughput under an SLA, alongside the standout GLM-5.2 model that excels in coding and reasoning capabilities. The cutting-edge technology from Wafer utilizes agents that optimize inference across the entire stack, effectively identifying and resolving bottlenecks in orchestration, algorithms, serving engines, GPU kernels, and various hardware configurations. This advanced system conducts a thorough profiling of the stack to ascertain whether latency or throughput problems stem from areas such as scheduling, decoding, memory pressure, or hardware compatibility, subsequently exploring multiple avenues to provide the most effective resolutions. Instead of relying on a single switch or heuristic, Wafer performs an exhaustive examination of various combinations of models, engines, kernels, and hardware to enhance overall performance. By continually honing these combinations, Wafer guarantees that enterprises can achieve maximum efficiency while making the most of open-source technologies, paving the way for unprecedented advancements in AI deployment. This dedication to innovation places Wafer at the forefront of the AI revolution, ensuring businesses remain competitive in a rapidly evolving digital landscape.

The Laguna XS 2.1 represents a sophisticated advancement in coding models, functioning as an open weight agentic system that excels in executing long-duration tasks on local machines. It boasts a robust 33-billion-parameter Mixture-of-Experts architecture, activating 3 billion parameters per token, while preserving the efficient design of its predecessor, Laguna XS.2, and significantly enhancing its capabilities in multilingual software engineering and terminal-related tasks. This model is meticulously crafted to support coding agents in reviewing code repositories, navigating complex changes, leveraging diverse tools, executing commands, and ensuring seamless progress throughout extensive projects. With an impressive context window of 256K, it empowers agents to adeptly handle large codebases, maintain extensive histories, and navigate intricate multi-step workflows. The Laguna XS 2.1 also enjoys compatibility with various platforms like vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with aspirations for future native support from llama.cpp. Offered in multiple checkpoint formats such as BF16, FP8, INT4, and NVFP4, it allows developers to choose between high fidelity and configurations designed for environments with restricted VRAM or processing capacity. This versatility not only enhances its usability across different development frameworks but also positions it as a prime choice for diverse programming needs and settings. Furthermore, its ability to adapt to varying project demands makes it a valuable asset for developers seeking efficiency and performance in their workflows.

CodeT5 is a cutting-edge pre-trained encoder-decoder model crafted specifically for the tasks of code comprehension and generation. This model is designed to be aware of identifiers and serves as a comprehensive framework suitable for a variety of coding challenges. Its official implementation in PyTorch stems from a research paper introduced by Salesforce Research at EMNLP 2021. Among its notable versions is CodeT5-large-ntp-py, which has been fine-tuned to achieve outstanding performance in Python code generation, serving as the foundation for our CodeRL strategy and securing impressive results in the APPS Python competition-level program synthesis benchmark. The repository contains all the necessary resources to replicate the experiments performed with CodeT5. Trained on a vast dataset consisting of 8.35 million functions across eight different programming languages—such as Python, Java, JavaScript, PHP, Ruby, Go, C, and C#—CodeT5 has shown remarkable performance, setting state-of-the-art results across 14 distinct sub-tasks in the code intelligence benchmark referred to as CodeXGLUE. Additionally, its ability to produce code directly from natural language input highlights both its adaptability and efficacy in programming contexts, making it a valuable tool for developers and researchers alike.

CodeQwen acts as the programming equivalent of Qwen, a collection of large language models developed by the Qwen team at Alibaba Cloud. This model, which is based on a transformer architecture that operates purely as a decoder, has been rigorously pre-trained on an extensive dataset of code. It is known for its strong capabilities in code generation and has achieved remarkable results on various benchmarking assessments. CodeQwen can understand and generate long contexts of up to 64,000 tokens and supports 92 programming languages, excelling in tasks such as text-to-SQL queries and debugging operations. Interacting with CodeQwen is uncomplicated; users can start a dialogue with just a few lines of code leveraging transformers. The interaction is rooted in creating the tokenizer and model using pre-existing methods, utilizing the generate function to foster communication through the chat template specified by the tokenizer. Adhering to our established guidelines, we adopt the ChatML template specifically designed for chat models. This model efficiently completes code snippets according to the prompts it receives, providing responses that require no additional formatting changes, thereby significantly enhancing the user experience. The smooth integration of these components highlights the adaptability and effectiveness of CodeQwen in addressing a wide range of programming challenges, making it an invaluable tool for developers.

Jectora acts as a centralized AI interface that provides users with the ability to engage with a cohesive OpenAI-compatible API, facilitating requests directed towards multiple model providers. Developers have the option to work with either the OpenAI JavaScript or Python SDK, adjusting the base URL and model ID as necessary, while accessing various endpoints designated for chat, responses, and embeddings. A detailed model catalog is offered, showcasing the available models, their attributes, pricing, and service health indicators. Jectora adeptly oversees requests between different model providers and incorporates API-key controls that regulate model access, expenditure thresholds, expiration timelines, and source IP limitations. Tools for organization enhance collaborative management within teams, allowing oversight of shared access, usage metrics, account balances, and billing systems. Operating on a prepaid, pay-as-you-go credit model, costs are based on the selected model and its corresponding input, output, or media consumption. This platform has been specifically tailored for developers and organizations looking to build AI-driven applications that necessitate a standardized API alongside efficient model usage management. Furthermore, Jectora's adaptability enables swift modifications to meet changing project demands, thereby positioning it as an essential asset for cutting-edge development endeavors. Such versatility not only enhances operational efficiency but also fosters innovation in various applications.

Mistral 7B is a cutting-edge language model boasting 7.3 billion parameters, which excels in various benchmarks, even surpassing larger models such as Llama 2 13B. It employs advanced methods like Grouped-Query Attention (GQA) to enhance inference speed and Sliding Window Attention (SWA) to effectively handle extensive sequences. Available under the Apache 2.0 license, Mistral 7B can be deployed across multiple platforms, including local infrastructures and major cloud services. Additionally, a unique variant called Mistral 7B Instruct has demonstrated exceptional abilities in task execution, consistently outperforming rivals like Llama 2 13B Chat in certain applications. This adaptability and performance make Mistral 7B a compelling choice for both developers and researchers seeking efficient solutions. Its innovative features and strong results highlight the model's potential impact on natural language processing projects.

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

RunInfra revolutionizes the process of converting natural language inputs into fully functional AI inference endpoints with remarkable ease. By merely expressing your project’s needs, the AI agent takes charge of constructing, refining, deploying, and scaling the solution without requiring any YAML configurations, DevOps skills, or GPU setups—it's all done through a simple dialogue. Tailored for producing open-source AI models as ready-to-use APIs, it adeptly selects the most appropriate models, evaluates the actual performance of GPUs, incorporates kernel improvements, and sets up HTTP endpoints that work seamlessly with OpenAI. RunInfra has the versatility to develop a wide range of applications, such as language models, speech recognition systems, text-to-speech technologies, embeddings, vision-language tasks, image generation, retrieval-augmented generation (RAG) searches, document analysis, transcription services, AI assistants, and intricate multi-model reasoning frameworks, all depending on the capabilities of the runtime and models employed. Its user-friendly workflow transitions smoothly from your initial input through to optimization, deployment, and integration; just communicate your requirements to RunInfra, and it will assess real GPU options from L4 to B200, investigate model variations like AWQ, GPTQ, and FP8, fine-tune kernels with Forge, and provide a fully operational endpoint that is compatible with OpenAI’s Python and JavaScript SDKs. The remarkable efficiency and straightforwardness of RunInfra position it as an essential tool for developers eager to harness cutting-edge AI technologies without facing the usual challenges associated with such tasks. Moreover, the platform's ability to simplify complex processes not only saves time but also empowers teams to focus on innovation rather than technical hurdles.

The Nexa SDK is an all-encompassing toolkit for developers, empowering them to execute and deploy various AI models locally on a broad spectrum of devices that have NPUs, GPUs, and CPUs, enabling efficient functioning without dependence on cloud services. It boasts a swift command-line interface, Python bindings, and mobile SDKs tailored for both Android and iOS platforms, and it is also compatible with Linux, allowing developers to easily integrate AI features into applications, IoT devices, automotive technologies, and desktop environments with minimal configuration, requiring just a single line of code to run models. Furthermore, it offers an OpenAI-compatible REST API and function calling capabilities, streamlining the integration with pre-existing client systems. The innovative NexaML inference engine, meticulously engineered for peak performance across diverse hardware setups, supports a variety of model formats, including GGUF, MLX, and its proprietary format. Additionally, the SDK encompasses comprehensive multimodal support, addressing a wide array of tasks related to text, images, and audio, which includes features like embeddings, reranking, speech recognition, and text-to-speech. Importantly, the SDK prioritizes Day-0 support for the latest architectural innovations, ensuring that developers remain at the cutting edge of AI advancements. This extensive array of features not only enhances the functionality of the Nexa SDK but also establishes it as a vital resource for developers aiming to create state-of-the-art AI applications. With each update, Nexa SDK continues to evolve, adapting to the changing landscape of technology and user needs.

Incbury is an independent AI company based in the UK that develops its proprietary compact language models known as Atom. Their tool, Incbury Chat, serves as an AI assistant intended for a variety of tasks including conversation, writing, research, and analysis, alongside features for web searches, project management, and file management. Moreover, Incbury Code acts as a coding assistant that improves workflows on desktops and terminals, equipped with sidebars for popular platforms like VS Code and Cursor; it can analyze codebases, propose changes for consideration, and run commands and tests. The Incbury API provides developers with OpenAI-compatible access to Atom, allowing for the incorporation of language model capabilities within various applications, including functionalities such as streaming and function calling. Users can access the Chat tool through a complimentary plan that comes with daily fair-use restrictions, while the API and Code services function on a prepaid credit model. Furthermore, Incbury publishes benchmark results along with case studies that demonstrate the limitations of its models. With its wide array of offerings, Incbury is well-suited for individuals, developers, startups, and teams seeking chat help, coding assistance, or AI-integrated solutions, thus establishing itself as a flexible player in the evolving AI market. The company’s commitment to enhancing user experience and providing valuable insights further solidifies its position in the industry.

Luminal is an advanced machine-learning framework that prioritizes performance, ease of use, and modularity, utilizing static graphs and compiler-based optimization techniques to handle intricate neural networks efficiently. By converting models into a streamlined set of minimal "primops," consisting of only 12 essential operations, Luminal can perform compiler passes that replace these with optimized kernels suited for particular devices, enabling high-performance execution on GPUs and other hardware platforms. The framework features modules that act as the core building blocks of networks, complemented by a standardized forward API and the GraphTensor interface, which allows for the definition and execution of typed tensors and graphs during compile time. With a focus on maintaining a small and adaptable core, Luminal promotes extensibility through the incorporation of external compilers that support diverse datatypes, devices, training methodologies, and quantization strategies. To facilitate user adoption, a quick-start guide is provided, helping users to clone the repository, build a straightforward "Hello World" model, or run more complex models such as LLaMA 3 with GPU support, simplifying the process for developers looking to tap into its capabilities. Overall, Luminal's flexible architecture positions it as a formidable resource for both newcomers and seasoned experts in the field of machine learning, bridging the gap between simplicity and advanced functionality.

Recursive Flint is a state-of-the-art language model boasting 40 billion parameters, crafted as a mixture-of-experts particularly for local coding tasks. It utilizes approximately 2.5 billion parameters for every token it processes, allowing for advanced reasoning, multi-turn tool usage, and support for context windows reaching up to 512,000 tokens. Developers have the opportunity to integrate Flint into coding agents, enabling them to inspect codebases, fix bugs, create new features, and formulate tests while running inference directly on their own systems. The model's weights are accessible under the Apache 2.0 license, fostering broader accessibility for users. Flint supports a range of platforms, including llama.cpp, Ollama, Unsloth, LM Studio, vLLM, and SGLang. In addition, Recursive provides rec, an inference engine finely tuned for optimal performance on Apple Silicon M4 Pro and M5 Pro/Max chips. To facilitate the implementation of the model on local machines, detailed setup documentation and practical examples are available for developers. This versatile tool not only aids developers and technical teams in running a code-focused model tailored to their individual system needs but also enhances their overall coding efficiency. As a result, users can significantly boost their development workflows and achieve their programming goals with greater ease and speed than ever before.

The Mixtral 8x7B model represents a cutting-edge sparse mixture of experts (SMoE) architecture that features open weights and is made available under the Apache 2.0 license. This innovative model outperforms Llama 2 70B across a range of benchmarks, while also achieving inference speeds that are sixfold faster. As the premier open-weight model with a versatile licensing structure, Mixtral stands out for its impressive cost-effectiveness and performance metrics. Furthermore, it competes with and frequently exceeds the capabilities of GPT-3.5 in many established benchmarks, underscoring its importance in the AI landscape. Its unique blend of accessibility, rapid processing, and overall effectiveness positions it as an attractive option for developers in search of top-tier AI solutions. Consequently, the Mixtral model not only enhances the current technological landscape but also paves the way for future advancements in AI development.

Qwen3.5-Plus is a next-generation multimodal large language model built for scalable, enterprise-grade reasoning and agentic applications. It combines linear attention mechanisms with a sparse mixture-of-experts architecture to maximize inference efficiency while maintaining performance comparable to leading frontier models. The system supports text, image, and video inputs, generating high-quality text outputs suited for analysis, synthesis, and tool-augmented workflows. With a 1 million token context window and support for up to 64K output tokens, Qwen3.5-Plus enables deep, long-form reasoning across extensive documents and datasets. Its optional deep thinking mode allows for expanded chain-of-thought reasoning up to 80K tokens, making it ideal for complex analytical and multi-step problem-solving tasks. Developers can integrate structured outputs, function calling, prefix continuation, batch processing, and explicit caching to optimize both performance and cost efficiency. Built-in tool support through the Responses API includes web search, web extraction, image search, and code interpretation for dynamic multi-agent systems. High throughput limits and OpenAI-compatible API endpoints make deployment straightforward across global applications. With transparent token-based pricing and enterprise-level monitoring, Qwen3.5-Plus provides a powerful foundation for building intelligent assistants, multimodal analyzers, and scalable AI services.

Cheaper Inference acts as an API gateway that is compatible with OpenAI, providing users with access to a diverse array of AI models from multiple providers through a single API key, thereby streamlining the request process without requiring any modifications in formatting. Developers can easily switch between different providers by merely updating the base URL and API key, all while keeping the same model, messages, tools, streaming configurations, and response management intact. This platform supports both text and image models, allows for vision-enabled chat requests, offers streaming functionalities, incorporates prompt caching, includes reasoning controls, and permits temporary image uploads for handling larger vision datasets. Each request allows users to select their desired model individually, and they can filter the available catalog by model type, vision features, reasoning options, streaming capabilities, or provider name. The system is equipped with automatic retry mechanisms to address network issues and provider errors, and it has fallback routes for eligible requests to minimize the risk of failures. Furthermore, every interaction is logged in the History section, which enables teams to monitor request volume, token usage, and overall operational activity, thus providing thorough oversight and management of AI engagements. This level of transparency not only aids in optimizing resource utilization but also helps in identifying and understanding usage patterns over time, making it a valuable tool for data-driven decision-making. Overall, Cheaper Inference enhances user experience by simplifying access to a multitude of AI resources while ensuring robust management and oversight.

Photon is the designated high-performance inference engine for Moondream, meticulously crafted to adeptly run vision-language models across diverse platforms such as cloud, desktop, and edge environments, all while maintaining real-time performance for AI applications in active production. This sophisticated engine operates as a tailored inference layer that integrates smoothly with the Moondream model framework, leveraging optimized scheduling, inherent image processing features, and specialized CUDA kernels to significantly boost speed and efficiency. As a result of this innovative design, Photon notably minimizes latency when compared to traditional configurations of vision-language models, enabling rapid interactions on edge devices and facilitating real-time data handling on server-grade systems. It is compatible with a wide array of NVIDIA GPUs, ranging from compact embedded systems like Jetson devices to robust multi-GPU servers, thus ensuring flexibility to accommodate a variety of operational requirements. Furthermore, Photon comes with production-ready functionalities such as automatic batching, prefix caching, and memory-optimized attention mechanisms, which enhance its performance in high-demand situations. These advanced features position it as an exceptional option for developers aiming to deploy AI-powered solutions in multiple environments, ensuring that they can address both current and future needs effectively. Ultimately, Photon's design and capabilities make it a compelling choice for those looking to harness the power of AI in diverse applications.

Mercury 2 signifies a revolutionary leap in reasoning models, particularly tailored for instantaneous voice interactions, as it can promptly respond to incoming calls. In contrast to conventional autoregressive models that often leave callers waiting in silence while they generate responses sequentially, Mercury 2 uses a diffusion large language model architecture that can produce more than 1000 tokens per second on standard NVIDIA GPUs. This extraordinary processing speed enables it to finalize a complete reasoning cycle and start speaking in a timeframe that harmonizes with the natural flow of conversation, effectively reducing the usual wait time from several seconds to around 300 milliseconds. The functionality of Mercury models revolves around converting clear text into noise, after which a traditional Transformer is trained to reverse this process and predict the original text simultaneously across all positions. By adopting a denoising strategy that processes multiple tokens concurrently, the generation process becomes more efficient, achieving speeds comparable to customized silicon on NVIDIA H100s while enhancing responsiveness in voice applications. Consequently, Mercury 2 not only improves user interactions but also establishes a new benchmark for the field of interactive voice technology, paving the way for future advancements. With its innovative design, it promises to revolutionize the way users engage with voice systems.

RelaySwitch functions as a comprehensive API gateway tailored for developers, allowing them to connect to AI models from multiple providers through a single endpoint and account. It provides an API that maintains compatibility with OpenAI and supports the Anthropic Messages format for Claude models, enabling effortless integration of preferred SDKs and tools by merely adjusting the base URL and API key. The platform boasts an extensive catalog featuring chat, image, video, speech, and 3D models, complete with detailed model identifiers and token pricing information. Billing operates on a prepaid USD balance system, where distinct rates are set for input and output, and users may find that leveraging cached input can be more cost-effective in certain scenarios. Users can top up their balance using several accepted payment methods, such as credit cards and digital assets, without the obligation of a monthly subscription. Specifically crafted for developers and teams focused on building applications and agents that utilize hosted AI models, RelaySwitch comes equipped with thorough API documentation, exhaustive model listings, and setup guides to ease integration with supported SDKs and tools. Furthermore, the platform is designed to optimize the development experience, making it increasingly straightforward for users to harness sophisticated AI capabilities within their projects while fostering innovation in application design.

Mirai stands out as a sophisticated platform designed specifically for developers, focusing on on-device AI infrastructure that facilitates the conversion, optimization, and execution of machine learning models right on Apple devices, all while prioritizing performance and user privacy. With a streamlined workflow, teams can effectively convert and quantize models, evaluate their performance, distribute them, and perform local inference without any hassle. Tailored for Apple Silicon, Mirai aims to deliver near-zero latency and eliminate inference costs, ensuring that the processing of sensitive data remains entirely on the user's device for enhanced security. Its comprehensive SDK and inference engine empower developers to quickly embed AI capabilities into their applications, utilizing hardware-aware optimizations to fully harness the potential of the GPU and Neural Engine. Additionally, Mirai incorporates dynamic routing features that smartly decide on the optimal execution path for tasks, whether it be executing locally or accessing cloud resources, while considering important factors like latency, privacy, and workload requirements. This adaptability not only improves the overall user experience but also equips developers with the tools to craft more responsive and efficient applications that cater specifically to the needs of their users, ultimately driving innovation in the realm of on-device AI.

Our systems are deployed worldwide to guarantee fast response times for users across the globe. Integrating our API, which is compatible with OpenAI, into your application is a straightforward process that requires minimal effort. You can initiate the integration in just a few minutes and scale it effectively to support millions of users. Our extensive scaling features and performance improvements mean that our API is four times more cost-efficient compared to the OpenAI GPT-3.5 API. Experience the capability to generate text and hold conversations with our AI model, delivering ChatGPT-like performance at a significantly lower cost. The setup process is quick, taking only a few minutes with our API. Moreover, you can leverage one of the most sophisticated AI image models available to create stunning, high-quality images, graphics, and illustrations in seconds, transforming your creative endeavors. This innovative approach not only optimizes your workflow but also significantly boosts your content creation productivity. By utilizing our platform, you can unlock new possibilities and elevate the quality of your work beyond traditional methods.