A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.Ultra excels at complex tasks like coding and deep research. Long-running agents spend their time planning, using tools, recovering from failures, and deciding what to do next.
NVIDIA just shipped Nemotron 3 Ultra, a 550B open frontier model purpose-built for long-running AI agents.
Most frontier reasoning models are optimised for single-turn accuracy. Agentic tasks are different: agents plan, call tools, delegate to sub-agents, handle failures, and pass history back into the model across many turns. As sessions get longer, token costs compound and models start losing the thread.
Nemotron 3 Ultra addresses this with a hybrid Mamba-Transformer architecture that handles long-context sequences without losing recall, and NVFP4 quantisation that delivers 5x higher throughput per GPU compared to BF16 on Blackwell.
Here's what ships:
550B total / 55B active parameters via LatentMoE so you get frontier reasoning without activating the full model on every token
Up to 1M token context window handles large codebases, long tool-call chains, and multi-document synthesis natively
Multi-token prediction layers reduces generation time on long outputs and multi-turn workflows
Post-trained for OpenClaw, Hermes Agent, and LangChain Deep Agents accurate across agent harnesses, not just chat benchmarks
Multi-Teacher On-Policy Distillation trained with dense feedback from 10+ domain-specific teacher models across code, math, and tool use
Fully open weights, synthetic training data, and post-training recipes all released under OpenMDW-1.1
P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified →@rohanrecommends
Big release. What’s interesting to me is less the “bigger context window” headline and more what it means for actual agent runs, where most of the work is planning, tool calls, backtracking, and keeping state over time.
I’m curious how you’re seeing people use Nemotron 3 Ultra alongside retrieval or external memory. With a 1M context window, does that layer become less important, or does it just shift toward deciding what should live in memory vs what gets passed straight into the run?
A lot of frontier models are improving raw reasoning, but context management still feels like a separate bottleneck.
Have you seen longer-horizon agent workloads benefit more from the model improvements themselves, or from better retrieval and memory layers around them?
550B params (55B active), 1M context, 300 tok/sec. probably the strongest US open-weights model out there right now - and it's currently available for free on @Kilo Code
ouch.
About Nemotron 3 Ultra by NVIDIA on Product Hunt
“Powers faster, efficient reasoning for long-running agents”
Nemotron 3 Ultra by NVIDIA launched on Product Hunt on June 5th, 2026 and earned 169 upvotes and 4 comments, placing #7 on the daily leaderboard. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.Ultra excels at complex tasks like coding and deep research. Long-running agents spend their time planning, using tools, recovering from failures, and deciding what to do next.
Nemotron 3 Ultra by NVIDIA was featured in Developer Tools (514k followers) and Artificial Intelligence (470.9k followers) on Product Hunt. Together, these topics include over 171.9k products, making this a competitive space to launch in.
Who hunted Nemotron 3 Ultra by NVIDIA?
Nemotron 3 Ultra by NVIDIA was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Reviews
Nemotron 3 Ultra by NVIDIA has received 27 reviews on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
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Rohan Chaubey
NVIDIA just shipped Nemotron 3 Ultra, a 550B open frontier model purpose-built for long-running AI agents.
Most frontier reasoning models are optimised for single-turn accuracy. Agentic tasks are different: agents plan, call tools, delegate to sub-agents, handle failures, and pass history back into the model across many turns. As sessions get longer, token costs compound and models start losing the thread.
Nemotron 3 Ultra addresses this with a hybrid Mamba-Transformer architecture that handles long-context sequences without losing recall, and NVFP4 quantisation that delivers 5x higher throughput per GPU compared to BF16 on Blackwell.
Here's what ships:
550B total / 55B active parameters via LatentMoE so you get frontier reasoning without activating the full model on every token
Up to 1M token context window handles large codebases, long tool-call chains, and multi-document synthesis natively
Multi-token prediction layers reduces generation time on long outputs and multi-turn workflows
Post-trained for OpenClaw, Hermes Agent, and LangChain Deep Agents accurate across agent harnesses, not just chat benchmarks
Multi-Teacher On-Policy Distillation trained with dense feedback from 10+ domain-specific teacher models across code, math, and tool use
Fully open weights, synthetic training data, and post-training recipes all released under OpenMDW-1.1
P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends