NVIDIA Nemotron
NVIDIA's open-weight LLM family tuned for agentic workloads
Overview
Nemotron is NVIDIA's open-weight model family — Nano, Super and Ultra sizes on a hybrid Mamba-Transformer MoE architecture — built for reasoning and long-running agents and deployed through NVIDIA's NIM microservices.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 11 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- ~1,000 free API credits
- ~40 requests/min rate limit
- Hosted NIM endpoints for testing
- $0.04-$1.20 per 1M input tokens by model
- No infrastructure to manage
- Scales with usage
- Enterprise support SLA
- 90-day free production trial
- Multi-year and education/Inception discounts
Use cases
What you can produce with NVIDIA Nemotron
- Nano, Super and Ultra model sizes for different latency/accuracy trade-offs
- Hybrid Mamba-Transformer Mixture-of-Experts architecture
- Up to 1M-token context window
- Deployable as NVIDIA NIM microservices on any GPU-accelerated system
- Open weights on Hugging Face under the NVIDIA org
- Reward- and safety-tuned variants for alignment work
- Synthetic data generation and alignment tooling
ASEAN Perspective
NVIDIA Nemotron in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Nemotron 3, released June 2026, is the current generation: Nano, Super and Ultra variants sharing a hybrid Mamba-Transformer mixture-of-experts design with up to a 1M-token context. Nemotron 3 Ultra is the flagship — 550B parameters, pitched at long-running agentic tasks, with NVIDIA claiming 5x faster inference and up to 30% lower cost than other open frontier models on comparable work. Independent benchmarking (Artificial Analysis) put it at the top of the US open-weight pack in one snapshot. The real value proposition is deployment: these ship as NIM microservices tuned for NVIDIA hardware, so if you're already running GPUs, Nemotron slots in with far less friction than a generic open checkpoint. Outside that ecosystem the appeal is thinner. It suits enterprises and ML teams with NVIDIA infrastructure and MLOps maturity already in place; a lighter-weight team without that footprint won't see the same payoff.
What people say
CodeRabbit's benchmark of Nemotron 3 Ultra against its own review baseline found quality "close" and speed competitive — a mean latency of 7 minutes 6 seconds per full review trace versus 8 minutes 31 seconds for the baseline model — but flagged that Ultra carried a heavier retry burden to get there, concluding the "reliability control loop needs work." That's a fair summary of where Nemotron sits generally: fast and cheap on paper, with rough edges that show up once you push it into a real pipeline rather than a benchmark harness.
Developer commentary treats Nemotron less as a chat model to evaluate on vibes and more as infrastructure — something to slot into terminals, code review bots, test generators and coding agents where explicit instructions and external checks compensate for the model's own weaker judgment. That framing matches how NVIDIA positions it: not a leaderboard flex, but a component in an agent stack.
Access has three tiers. NVIDIA's developer tier gives free API credits (roughly 1,000 credits, capped around 40 requests/minute) for prototyping through build.nvidia.com — enough to kick the tires, not enough for production traffic. Above that, hosted NIM endpoints are billed per token, ranging from $0.04 to $1.20 per million input tokens depending on model size (Nemotron 3 Nano at the cheap end, older 70B variants at the expensive end). For self-hosted production use you need an NVIDIA AI Enterprise license, priced from $4,500 per GPU per year in NVIDIA's published guide, with a 90-day free trial and negotiated multi-year or education/Inception-program discounts — though actual enterprise contract pricing isn't published and varies by deal.
The open-weight release itself is free to download and self-host outside NIM entirely, which is the appeal for teams that just want the checkpoints without NVIDIA's serving stack. The catch is that Nemotron's real advantages — the 5x inference speedup and cost claims — are measured on NVIDIA's own hardware and software stack, so numbers won't necessarily transfer if you're running elsewhere.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including NVIDIA Nemotron's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with NVIDIA Nemotron unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
For the latest details, please refer to NVIDIA Nemotron directly →
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