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About

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.

About

North Mini Code is Cohere’s first agentic coding model for developers and the inaugural member of its next generation of powerful models. Small, efficient, and open-source, it is built for the sovereign developer ecosystem and designed to deliver strong software development performance without requiring extensive hardware. North Mini Code is a mixture-of-experts model with 30B total parameters and 3B active parameters, giving developers access to agentic coding capabilities in a compact and efficient form. The model is optimized for code generation, agentic software engineering, and terminal tasks, with a 256K total context length and up to 64K maximum generation. It is built for real-world developer workflows, including understanding and orchestrating sub-agents, mapping system architecture, running code reviews, and supporting coding agents that need to reason through complex software tasks.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

AI developers, software engineers, machine learning researchers, game developers, robotics engineers, enterprise IT teams, and organizations building local autonomous agents, coding assistants, conversational AI systems, and privacy-conscious applications on NVIDIA hardware

Audience

Developers seeking to run efficient open-source AI models for code generation, agentic software engineering, terminal tasks, and code reviews

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
features 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Pros & Cons from Real Users

Pros

  • What 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.

Cons

  • The 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.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Supported
Live Online Supported
In Person Not Supported

Company Information

NVIDIA
Founded: 1993
United States
nvidia.com

Company Information

Cohere
Founded: 2019
Canada
cohere.com/blog/north-mini-code

Alternatives

Alternatives

GLM-5.2

GLM-5.2

Z.ai
SWE-2

SWE-2

Cognition
Kimi K2

Kimi K2

Moonshot AI

Categories

AI Coding Models Supported
AI Models Supported
Foundation Models Supported
Multimodal Models Supported

Categories

AI Coding Models Supported
AI Models Supported

Integrations

Baseten Supported
Hugging Face Transformers Supported
NVIDIA NIM Supported
OpenCode Not Supported
OpenRouter Supported
vLLM Supported

Integrations

Baseten Not Supported
Hugging Face Transformers Not Supported
NVIDIA NIM Not Supported
OpenCode Supported
OpenRouter Not Supported
vLLM Not Supported
Claim Nemotron 3.5 Bolt and update features and information
Claim Nemotron 3.5 Bolt and update features and information
Claim North Mini Code and update features and information
Claim North Mini Code and update features and information