What is Nemotron 3.5 Bolt?
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.
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Really good/fast model for agents
Date: Oct 15 2026SummaryOverall, Nemotron 3.5 Bolt is one of the most practical local agent models I have seen. It gives me strong reasoning and agent capability with much more privacy, control, and deployment flexibility than a cloud-only model.
PositiveWhat I like most is that I can run a genuinely capable agent model locally without needing datacenter hardware. The 74B-A9B version fits in 24GB of memory, which makes it realistic for high-end RTX systems and DGX Spark. It is also surprisingly useful for real agent work. I can use it for research, coding, debugging, writing, and multi-step tasks while keeping everything on my own machine instead of pushing code or sensitive data to a cloud API. The performance-to-memory tradeoff is probably the biggest win. NVIDIA says Bolt 74B-A9B is the #1 open model in its class on PinchBench and #2 on BrowseComp, so it is not just small enough to run locally — it is actually competitive at agentic tasks.
NegativeThe main downside is that 24GB is still a meaningful hardware requirement, and local inference will not always match the raw capability of the biggest cloud frontier models.
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