Nemotron 3.5 Bolt
NVIDIA Nemotron 3.5 Bolt is a family of open artificial intelligence models designed for local AI agents, autonomous task execution, and conversational applications. The family includes Nemotron 3.5 Bolt 74B-A9B, a 74-billion-parameter mixture-of-experts model with approximately 9 billion active parameters, and Nemotron 3.5 Bolt E4B, a compact model optimized for conversational agents. The 74B-A9B variant uses native 2-bit quantization to operate within approximately 24GB of memory on compatible NVIDIA hardware. Its agentic capabilities support web research, software development, code debugging, and multistep task automation without requiring cloud-based inference. The E4B variant targets local voice assistants, interactive characters, and embedded applications with a 4GB memory footprint. Nemotron 3.5 Bolt supports deployment on NVIDIA RTX systems, DGX Spark, and other compatible hardware, giving developers greater control over inference, application data, and local AI workflows.
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Kimi K3
Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.
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Muse Spark Open
Muse Spark Open is an open-weight, natively multimodal language model for developers and organizations building AI applications, coding systems, and agentic workflows. The released model has 816 billion total parameters, 42 billion active parameters, and a 1-million-token context window. Teams can download the model weights, run inference through supported engines such as vLLM, SGLang, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, or Unsloth, and customize the model using PyTorch. It supports long-context reasoning, software engineering, coding, tool use, and multimodal inputs. Deployment options include cloud inference through listed API providers or self-hosting on compatible GPU systems; Meta documents a desktop setup using two Apple M5 Ultra Mac Studio computers. The model is released under the Muse Model License with limited commercial terms.
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Amp
Amp is a frontier coding agent built to give developers full access to the power of today’s leading AI models directly in their workflow. Available in the terminal and popular editors like VS Code, Cursor, Windsurf, JetBrains, and Neovim, Amp integrates seamlessly into existing development environments. It enables developers to delegate complex coding tasks, refactors, reviews, and explorations to intelligent agents that understand and operate across entire codebases. With support for advanced models such as Claude Opus, Gemini, and GPT-class models, Amp delivers fast, reliable, and highly agentic code generation. The platform is designed for real-world engineering work, handling multi-file changes, deep context, and iterative improvements. Amp helps developers move faster while maintaining confidence in code quality.
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