NVIDIA launched open applied sciences for constructing always-on AI brokers from techniques of specialised fashions. Two artifacts shipped collectively. Nemotron 3.5 Lightning is a light-weight, customizable open mannequin constructed for high-volume agentic duties, and NeMo Switchyard is an open supply routing library that directs every step of an agent workflow to essentially the most succesful and environment friendly mannequin obtainable. The issue each deal with is structural: long-running brokers spend most of their time on instrument calls, outcome validation, and subagent delegation, and sending each a type of steps to a frontier reasoning mannequin provides price and latency. Lightning is a 30B mixture-of-experts mannequin with 3B energetic parameters, constructed on a hybrid Mamba-2 + MoE + Consideration structure with a 1M-token context window. NVIDIA studies as much as 4x sooner output velocity than similar-sized fashions, and 30% sooner completion of 10,000 PinchBench duties than Qwen3.6 35B at comparable accuracy. Many business gamers like CrowdStrike, Harvey, CodeRabbit, Fastino Labs, and Lila Sciences are already customizing it for cybersecurity, authorized, coding, finance, and healthcare workloads.
Is it deployable?
Sure. Nemotron 3.5 Lightning is mostly obtainable below the permissive OpenMDW-1.1 license, with open weights, coaching information, and recipes. NVIDIA states the mannequin is prepared for industrial use.
- Which corporations: Anybody with a single trendy GPU. NVIDIA lists single-GPU deployment on 1x DGX Spark (GB10) or 1x H100. That places solo builders and seed-stage startups on the identical footing as enterprises. Mid-market groups can serve it from Baseten, Collectively AI, or Nebius; regulated enterprises can hold it totally on-premises.
- Industries: Cybersecurity, authorized providers, software program engineering, monetary providers, healthcare, and life sciences all seem in NVIDIA’s named buyer set.
- Purposes: Device calling, outcome validation, subagent delegation, code overview routing, log triage, contract parsing, and long-context retrieval throughout a 1M-token window.
The execution layer, not the planning layer
Lengthy-running brokers spend most of their time on high-volume execution. Device calls, outcome validation, and subagent delegation dominate the token price range. Routing each a type of steps to a frontier reasoning mannequin provides price and latency.
Nemotron 3.5 Lightning targets that execution layer. It’s a 30B mixture-of-experts mannequin with 3B energetic parameters, constructed on a hybrid Mamba-2 + MoE + Consideration structure. Context size reaches 1M tokens. Pre-training coated greater than 20 trillion tokens utilizing an NVFP4 recipe.
The mannequin is the smallest member of the Nemotron 3 household. Frontier fashions corresponding to Nemotron 3 Extremely deal with orchestration and planning, whereas Lightning handles the routine calls beneath them.
The place the velocity comes from
Two mechanisms:
- First, Speculative Decoding: Multi-token prediction was baked in throughout a devoted pre-training stage, then improved with an MTP-boosting part. NVIDIA additionally ships two exterior draft fashions: DSpark, a semi-autoregressive drafter really useful for DGX Spark and low-concurrency information middle workloads, and DFlash, which makes use of a light-weight block-diffusion mannequin.
- Second, Quantization: An NVFP4 checkpoint ships alongside BF16. The identical checkpoint serves Blackwell and Hopper natively, and extends to Ampere by W4A16 kernels.
NVIDIA studies as much as 4x output velocity versus similar-sized fashions. On PinchBench, it studies 86% accuracy whereas finishing 10,000 duties 30% sooner than Qwen3.6 35B at comparable accuracy.
Revealed mannequin card outcomes (BF16 / NVFP4): MMLU Professional 81.94 / 81.62, GPQA Diamond 75.44 / 75.57, SWE-bench Verified 51.56 / 52.80, Terminal-Bench 2.1 24.58 / 23.46, AA-LCR 52.00 / 49.19. Beneficial sampling is temperature 1.0 and top_p 0.95.
NeMo Switchyard
NeMo Switchyard is an open supply library that routes every step of an agent workflow to essentially the most succesful and environment friendly mannequin obtainable.
It affords tuning-free routers, together with an LLM classifier with session affinity, a stage router that reads current instrument exercise, and an escalation router that begins low-cost and promotes on sustained issue. A tunable prefill router learns from the mannequin’s residual stream to foretell which candidate will succeed. The reference server accepts OpenAI, Anthropic, and Responses API requests.
Two printed outcomes: LangChain benchmarked 145 multi-turn agentic duties. Routing between Lightning and Claude Opus 4.8 with the escalation router minimize price 74% versus a frontier-only baseline, sending 7% of calls to the frontier mannequin, at a roughly 6-point accuracy tradeoff. Cognition applied staged routing in Devin Desktop. On FrontierCode Essential, routing between Opus 5 and Kimi K2.7 reached 50.6% at a $3.11 imply price, inside 2.8 factors of Opus 5 accuracy at roughly 28% decrease imply price.
Interactive explainer
Key Takeaways
- 30B open MoE with 3B energetic parameters, 1M context, OpenMDW-1.1 license, industrial use permitted.
- As much as 4x output velocity; PinchBench 10,000 duties accomplished 30% sooner than Qwen3.6 35B.
- Pace comes from multi-token prediction plus DSpark and DFlash drafters, and an NVFP4 checkpoint.
- Runs on 1x DGX Spark or 1x H100, and domestically through Ollama, LM Studio, llama.cpp, and Unsloth.
- NeMo Switchyard minimize price 74% in LangChain’s 145-task benchmark at a ~6-point accuracy tradeoff.
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