NVIDIA Nemotron 3 Nano 30B A3B is 30B total with 3B active, which is small enough to be nearly free and still reason. DeepInfra serves it at $0.05/$0.20 per Mtok, Vercel AI Gateway at $0.05/$0.24, Nebius at $0.06/$0.24, and NVIDIA NIM lists it at $0.00/$0.00 on a free tier. Context runs from 131,072 tokens on NVIDIA NIM up to 1,048,576 tokens on Ollama Cloud, an absurd window for a model this size. Inside Atlas, Nemotron 3 Nano belongs in the `small_model` slot, powering titles, summaries, and subagents for pennies.
What is NVIDIA Nemotron 3 Nano 30B A3B best at inside Atlas?
NVIDIA Nemotron 3 Nano 30B A3B is best inside Atlas as the `small_model`, the slot that handles titles, summaries, and subagent scaffolding. At $0.05/$0.20 per Mtok on DeepInfra, 3B active parameters out of 30B total means it runs at small-model latency and small-model price while retaining a reasoning mode.
Every Atlas session generates overhead calls that have nothing to do with writing code: session titles, conversation summaries, subagent scaffolding. Routing those through a frontier model is money burned. Nemotron 3 Nano at $0.05 per Mtok input makes that overhead round to zero. What separates Nemotron 3 Nano from other cheap models is that it keeps a reasoning mode at 3B active parameters, so subagent work that needs a little judgment does not immediately fall over. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and pointing that fan-out at a $0.05/$0.20 per Mtok model is what makes aggressive parallelism affordable at all.
How cheap is Nemotron 3 Nano 30B A3B, and can you try it for free?
NVIDIA Nemotron 3 Nano 30B A3B is listed at $0.00/$0.00 per Mtok on NVIDIA NIM, so you can validate it against your codebase before spending anything. Paid routes are DeepInfra at $0.05/$0.20 per Mtok, Vercel AI Gateway at $0.05/$0.24, and Nebius at $0.06/$0.24.
The free NVIDIA NIM tier for Nemotron 3 Nano is the honest way to evaluate it. Export NVIDIA_API_KEY, point Atlas at the model, and run it against a real repository before committing any budget. If it holds up, switch to DeepInfra for the $0.05/$0.20 listing. DeepInfra serves it with max output equal to its full 262,144 token context at $0.05/$0.20 per Mtok, so it can emit a very large diff for almost nothing. That combination, a huge output ceiling at a fifth of a cent per thousand output tokens, is what makes Nemotron 3 Nano genuinely useful rather than merely cheap. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, so a large emitted diff still lands in front of you before it touches disk.
Is the 1,048,576 token context on Nemotron 3 Nano real?
Ollama Cloud exposes NVIDIA Nemotron 3 Nano 30B A3B at a 1,048,576 token context, which is an absurd window for a 30B model. DeepInfra and Vercel AI Gateway serve it at 262,144 tokens, and NVIDIA NIM at 131,072. Treat the million-token listing with skepticism.
The 1,048,576 token window on Ollama Cloud is a serving configuration, not a capability claim. Retrieval quality that deep into the context is not something the model card guarantees for Nemotron 3 Nano, and a 3B-active model is not the model you should be asking to find a needle at token 900,000. The better strategy inside Atlas is to give it less context and better context. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and Atlas indexes code by AST declarations using tree-sitter, not blind line windows. Good retrieval into a 262,144 token window on DeepInfra will beat blind stuffing of a 1,048,576 token window every time.
What are the limits of a 3B-active model in a coding agent?
NVIDIA Nemotron 3 Nano 30B A3B activates only 3B parameters per token, and a 3B-active model will lose to Kimi K2.7 Code or GLM-5.2 on multi-file refactors that require holding a lot of cross-file invariants at once. Nemotron 3 Nano is an assistant to the build agent, not the build agent.
Honesty about Nemotron 3 Nano's ceiling is what makes the $0.05/$0.20 per Mtok price useful rather than a trap. The model reasons, but it reasons at 3B active parameters, and cross-file invariants are precisely the thing that scale buys you. Keep a larger model in the `"model"` slot for the build agent, and let `/models` switch between them. Atlas lets you switch the active model and provider on the fly with favorites and recents, so a Nano-plus-frontier pairing is one config, not two workflows. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, which is worth keeping in mind if you do decide to let a Nano-class model drive a build agent on an experimental branch.
How do you split Nemotron 3 Nano and a frontier model in atlas.json?
In Atlas, set `"small_model": "deepinfra/nvidia/Nemotron-3-Nano-30B-A3B"` so titles, summaries, and subagents run at $0.05 per Mtok, and keep `"model"` on a larger model for the build agent. That one split is the entire reason Nemotron 3 Nano exists in a coding agent.
The two-slot config is where Nemotron 3 Nano pays for itself. Atlas routes overhead calls to `small_model` and real work to `model`, so pinning Nemotron 3 Nano to the cheap slot removes an entire category of spend without changing what the build agent can do. Run `atlas models deepinfra` and look for the `nvidia/Nemotron-3-Nano-30B-A3B` row to confirm the model resolved before editing atlas.json. Atlas snapshots file changes as git patches so edits can be diffed and rolled back, and Atlas reads git branches, status, and diffs, and can stage and create commits on your behalf, so even the commit messages Nemotron 3 Nano drafts arrive as reviewable output rather than silent writes.
Setup
- 01Export DEEPINFRA_API_KEY for the $0.05/$0.20 listing, or NVIDIA_API_KEY to trial it at $0.
- 02Run `atlas models deepinfra` and look for the `nvidia/Nemotron-3-Nano-30B-A3B` row.
- 03Set `"small_model": "deepinfra/nvidia/Nemotron-3-Nano-30B-A3B"` in atlas.json so titles, summaries, and subagents run at $0.05 per Mtok.
- 04Keep a larger model in the `"model"` slot for the build agent, and let `/models` switch between them.
- 05Validate Nemotron 3 Nano against your own repo on the NVIDIA NIM free tier at $0.00/$0.00 per Mtok before moving spend to DeepInfra.
Frequently asked questions
- how much does nemotron 3 nano 30b cost
- Nemotron 3 Nano 30B A3B is $0.05/$0.20 per Mtok on DeepInfra, $0.05/$0.24 on Vercel AI Gateway, $0.06/$0.24 on Nebius, and $0.00/$0.00 on the NVIDIA NIM free tier.
- is nemotron 3 nano free to use
- Yes, on NVIDIA NIM. Nemotron 3 Nano 30B A3B is listed at $0.00/$0.00 per Mtok there, so you can validate it against your codebase before spending anything. Export NVIDIA_API_KEY to trial it at $0.
- what is the context window of nemotron 3 nano 30b a3b
- Nemotron 3 Nano holds 1,048,576 tokens on Ollama Cloud, 262,144 on DeepInfra and Vercel AI Gateway, and 131,072 on NVIDIA NIM. The million-token listing is a serving configuration, not a capability claim.
- how do I set nemotron 3 nano as atlas small_model
- Export DEEPINFRA_API_KEY, run `atlas models deepinfra` and find the `nvidia/Nemotron-3-Nano-30B-A3B` row, then set `"small_model": "deepinfra/nvidia/Nemotron-3-Nano-30B-A3B"` in atlas.json.
- can nemotron 3 nano handle multi file refactors
- No. A 3B-active model will lose to Kimi K2.7 Code or GLM-5.2 on multi-file refactors that require holding a lot of cross-file invariants at once. Keep a larger model in the `"model"` slot for the build agent.
- does nemotron 3 nano support reasoning
- Yes. Nemotron 3 Nano 30B A3B retains a reasoning mode despite activating only 3B of its 30B parameters per token, which is unusual at a $0.05/$0.20 per Mtok price point.
- cheapest model for atlas subagents
- Nemotron 3 Nano 30B A3B on DeepInfra at $0.05/$0.20 per Mtok is a strong choice. Atlas fans out work to subagents that run in the foreground or in parallel background sessions, and routing that fan-out through Nano keeps parallelism affordable.
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