What is the context window for NVIDIA Nemotron Nano 9B v2?

The context window for NVIDIA Nemotron Nano 9B v2 is 131,072 tokens when accessed via Vercel AI Gateway or NVIDIA NIM. On Amazon Bedrock, the context window is slightly reduced to 128,000 tokens.

How much does NVIDIA Nemotron Nano 9B v2 cost to use with Atlas?

NVIDIA Nemotron Nano 9B v2 costs $0.06/$0.23 per Mtok on Vercel AI Gateway and Amazon Bedrock. It is available on NVIDIA NIM with a free tier, costing $0.00/$0.00 per Mtok.

Can NVIDIA Nemotron Nano 9B v2 be used for code generation in Atlas?

No, NVIDIA Nemotron Nano 9B v2's 9B parameters are not sufficient for the build agent's code generation and diff-writing tasks. It is best suited for orchestration and summarization, allowing your primary model to focus on complex code modifications.

What are the differences between providers for NVIDIA Nemotron Nano 9B v2?

Vercel AI Gateway offers a 131,072 token context with max output equal to its full context. Amazon Bedrock provides 128,000 tokens context but only 4,096 max output. NVIDIA NIM offers a free tier with a 131,072 token context.

How do I configure Atlas to use NVIDIA Nemotron Nano 9B v2 as the small model?

First, configure your API key (Vercel) or AWS credentials (Bedrock). Then, set `"small_model": "vercel/nvidia/nemotron-nano-9b-v2"` (or the Bedrock equivalent) in your `atlas.json` file, ensuring your primary `"model"` remains set to your frontier pick.

Does Atlas use NVIDIA Nemotron Nano 9B v2 for code search?

Atlas searches code with Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion. While NVIDIA Nemotron Nano 9B v2 handles summarization and scaffolding, it is not directly involved in the code search mechanism itself. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers.

Can Atlas use NVIDIA Nemotron Nano 9B v2 for local indexing?

Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers. NVIDIA Nemotron Nano 9B v2 is a hosted model and is not used for local indexing; its role is for hosted reasoning tasks like summarization and orchestration.

Atlas with NVIDIA Nemotron Nano 9B v2 in 2026

NVIDIA Nemotron Nano 9B v2 is ideal for Atlas's small_model slot, handling tasks like session titles, summaries, and subagent orchestration without impacting your primary model budget. It provides a 131,072 token context window on Vercel AI Gateway for just $0.06/$0.23 per Mtok, making it a highly efficient choice for background operations in 2026.

What is NVIDIA Nemotron Nano 9B v2 best for in Atlas?

NVIDIA Nemotron Nano 9B v2 excels as the small_model in Atlas, specifically designed for tasks like generating session titles, summarizing content, and orchestrating subagents. Its 9B parameters provide reasoning capabilities at a cost-effective rate of $0.06/$0.23 per Mtok, making it an efficient choice for background operations in 2026.

This model is purpose-built to manage the lighter, yet crucial, cognitive load within Atlas. It handles the scaffolding for subagents, ensuring that complex tasks are broken down and managed efficiently. For instance, when Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, NVIDIA Nemotron Nano 9B v2 can draft the initial plan in a read-only plan agent and ask for approval before switching to a build agent. It also generates concise summaries and titles for your Atlas sessions, keeping your main, more expensive model focused on core code generation and modification. This strategic allocation of tasks ensures that your development workflow remains fluid and budget-conscious.

What are the cost and context window tradeoffs for NVIDIA Nemotron Nano 9B v2?

NVIDIA Nemotron Nano 9B v2 presents distinct cost and context window tradeoffs depending on the provider in 2026. On Vercel AI Gateway, it offers a generous 131,072 token context window with max output equal to its full context, priced at $0.06/$0.23 per Mtok.

While the model is generally cost-effective, its performance and utility vary by hosting provider. Vercel AI Gateway provides the most advantageous offering, serving the model with a full 131,072 token context window and an unusual max output equal to that full context, all for $0.06/$0.23 per Mtok. This makes it a strong contender for tasks requiring extensive context processing within its designated small_model role. In contrast, Amazon Bedrock also lists NVIDIA Nemotron Nano 9B v2 at $0.06/$0.23 per Mtok, but it trims the context window to 128,000 tokens and limits max output to only 4,096 tokens. This significantly reduces its utility for output-heavy tasks compared to the Vercel offering. NVIDIA NIM offers a free tier for this model, providing the same 131,072 token context window as Vercel, making it an excellent option for initial exploration or very low-volume use. Developers must weigh these specific differences when integrating NVIDIA Nemotron Nano 9B v2 into their Atlas workflow.

When should I choose a different model for Atlas's build agent?

You should choose a different, more powerful model for Atlas's build agent when the task involves writing code or generating complex diffs, as NVIDIA Nemotron Nano 9B v2's 9B parameters are not sufficient for this role. This model is specifically designed for orchestration and summarization, not for direct code modification in 2026.

NVIDIA Nemotron Nano 9B v2 is optimized for the small_model slot, handling background tasks and agent scaffolding. Its 9B parameter count, while capable of reasoning, is not robust enough for the demanding requirements of the build agent, which is responsible for generating and applying code changes. For tasks where Atlas computes a unified diff for every file edit and surfaces it for approval before writing, or when Atlas reads git branches, status, and diffs, and can stage and create commits on your behalf, a larger, more capable model should be selected. The small_model slot is intended to offload less intensive operations, preserving your main budget for the heavy lifting of code generation and modification. Atlas lets you switch the active model and provider on the fly, so you can easily configure a more powerful model for your primary model slot while retaining NVIDIA Nemotron Nano 9B v2 for its cost-effective orchestration capabilities.

Setup

011: Configure API Key or AWS Credentials: For Vercel AI Gateway, export your API key: `export AI_GATEWAY_API_KEY="your_vercel_api_key"`. For Amazon Bedrock, configure your AWS credential chain using `AWS_PROFILE` or by setting `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`.
022: Discover the Model: Run `atlas models vercel` (or `atlas models bedrock` for AWS) to locate the `nvidia/nemotron-nano-9b-v2` row and confirm its availability.
033: Set as Small Model: Open your `atlas.json` configuration file and add or update the `"small_model"` entry to `"small_model": "vercel/nvidia/nemotron-nano-9b-v2"` (or the appropriate Bedrock path).
044: Maintain Frontier Pick: Ensure your primary `"model"` setting in `atlas.json` remains configured with your preferred frontier model for core code generation tasks.
055: Verify Split Operation: Confirm that the model split is active by observing Atlas session titles. These should now be generated without incurring calls to your more expensive primary model, indicating NVIDIA Nemotron Nano 9B v2 is handling the small_model responsibilities.

Frequently asked questions

What is NVIDIA Nemotron Nano 9B v2's primary role in Atlas?: NVIDIA Nemotron Nano 9B v2 is designed for Atlas's small_model slot, handling tasks like generating session titles, summarizing content, and orchestrating subagent scaffolding. It helps manage the cognitive load without using your main model budget.
What is the context window for NVIDIA Nemotron Nano 9B v2?: The context window for NVIDIA Nemotron Nano 9B v2 is 131,072 tokens when accessed via Vercel AI Gateway or NVIDIA NIM. On Amazon Bedrock, the context window is slightly reduced to 128,000 tokens.
How much does NVIDIA Nemotron Nano 9B v2 cost to use with Atlas?: NVIDIA Nemotron Nano 9B v2 costs $0.06/$0.23 per Mtok on Vercel AI Gateway and Amazon Bedrock. It is available on NVIDIA NIM with a free tier, costing $0.00/$0.00 per Mtok.
Can NVIDIA Nemotron Nano 9B v2 be used for code generation in Atlas?: No, NVIDIA Nemotron Nano 9B v2's 9B parameters are not sufficient for the build agent's code generation and diff-writing tasks. It is best suited for orchestration and summarization, allowing your primary model to focus on complex code modifications.
What are the differences between providers for NVIDIA Nemotron Nano 9B v2?: Vercel AI Gateway offers a 131,072 token context with max output equal to its full context. Amazon Bedrock provides 128,000 tokens context but only 4,096 max output. NVIDIA NIM offers a free tier with a 131,072 token context.
How do I configure Atlas to use NVIDIA Nemotron Nano 9B v2 as the small model?: First, configure your API key (Vercel) or AWS credentials (Bedrock). Then, set `"small_model": "vercel/nvidia/nemotron-nano-9b-v2"` (or the Bedrock equivalent) in your `atlas.json` file, ensuring your primary `"model"` remains set to your frontier pick.
Does Atlas use NVIDIA Nemotron Nano 9B v2 for code search?: Atlas searches code with Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion. While NVIDIA Nemotron Nano 9B v2 handles summarization and scaffolding, it is not directly involved in the code search mechanism itself. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers.
Can Atlas use NVIDIA Nemotron Nano 9B v2 for local indexing?: Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers. NVIDIA Nemotron Nano 9B v2 is a hosted model and is not used for local indexing; its role is for hosted reasoning tasks like summarization and orchestration.

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