Nemotron 3.5 Bolt vs. Inkling 2 Small

NVIDIA Nemotron 3.5 Bolt is a family of open artificial intelligence models developed to provide efficient reasoning, autonomous agent capabilities, and conversational intelligence on local computing hardware. Introduced in October 2026, the series includes Nemotron 3.5 Bolt 74B-A9B 24GB and Nemotron 3.5 Bolt E4B 4GB. The 74B-A9B variant uses a mixture-of-experts architecture containing 74 billion total parameters, with approximately 9 billion parameters activated during inference. Its native 2-bit quantization enables deployment within approximately 24GB of memory while preserving resources for other applications. The model is designed for agentic workflows that require reasoning, instruction following, tool interactions, and execution of multiple connected tasks. Developers can use Bolt 74B-A9B with local agent frameworks to conduct web research, generate software, analyze code, investigate programming errors, and perform debugging activities. NVIDIA reports strong results on agent benchmarks including PinchBench and BrowseComp, which evaluate autonomous task completion and information retrieval capabilities. The smaller Bolt E4B variant is designed for low-latency conversational AI, supporting local voice assistants, interactive game characters, and embedded interfaces. Its 4GB memory footprint makes it suitable for compatible GeForce RTX, NVIDIA Jetson, and DGX Spark systems that require efficient local inference. Running these models locally allows organizations to retain greater control over sensitive application data, reduce dependence on cloud inference, and integrate AI capabilities into existing software environments. Nemotron 3.5 Bolt 74B-A9B is available through Hugging Face with support for llama.cpp, Ollama, LM Studio, Unsloth, and vLLM, while Bolt E4B is scheduled for release on October 23, 2026.

Inkling 2 Small is an advanced multimodal large language model developed by Thinking Machines Lab to support autonomous coding, complex reasoning, visual understanding, and customizable AI applications. The model uses a sparse mixture-of-experts transformer architecture containing 594 billion total parameters and 17 billion active parameters, with 48 decoder layers and 384 specialized experts. Its architecture combines local and global attention mechanisms with a vision transformer that processes images alongside text within a shared representation. Inkling 2 Small supports a context window of up to one million tokens, making it suitable for analyzing extensive documents, large software repositories, and long-running agent workflows. Configurable reasoning effort enables users to adjust the computational resources devoted to individual tasks based on their complexity and performance requirements. Its agentic software engineering capabilities include generating code, debugging applications, implementing features, verifying changes, and operating development tools across extended workflows. The model can also create interactive websites, games, three-dimensional simulations, and visual applications while using image-based feedback to evaluate and improve its results. Advanced research capabilities allow autonomous agents to conduct machine learning experiments, optimize GPU kernels, evaluate training strategies, and iteratively refine technical solutions. Thinking Machines Lab trained the model using 8.4 trillion pretraining tokens and a large reinforcement learning process involving more than 17 million rollouts across coding, scientific reasoning, visual tasks, knowledge work, and tool use. Published evaluation results include 25.4% on FrontierCode 2.0, 72.1% on DeepSWE 1.1, 86.3% on SWE-Bench Pro v2 Hard, and an Artificial Analysis Intelligence Index score of 31.