NVIDIA Nemotron 3 Super 120B A12B API
Released March 11, 2026 | 256K Tokens (up to 1M) context | 120B params (12B active) parameters
NVIDIA Nemotron 3 Super 120B A12B API enables Agentic workflows & multi-agent collaboration, Long-context reasoning (up to 1M tokens), IT ticket automation & high-volume enterprise workloads, Complex tool use & multi-step function calling, RAG (Retrieval-Augmented Generation), and Software engineering & cybersecurity triaging. NVIDIA Nemotron-3-Super-120B-A12B is an open-weight LLM built for agentic reasoning and high-volume workloads. Using a hybrid LatentMoE architecture (Mamba-2 + MoE + Attention) with Multi-Token Prediction (MTP) and native NVFP4 pretraining on 25T tokens, it delivers up to 2.2x higher throughput than GPT-OSS-120B and 7.5x higher than Qwen3.5-122B. With a native 1M-token context window and configurable thinking mode, it is purpose-built for collaborative agents, long-context reasoning, and IT automation across 7 languages. Standout strengths include LatentMoE: 512 experts / 22 active per token at same compute cost as standard MoE and 2.2x throughput vs GPT-OSS-120B; 7.5x vs Qwen3.5-122B. It is optimized for production agent and assistant workloads where response quality, latency, and predictable operating cost all matter.
from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=16000, temperature=1, top_p=0.95, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n")from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=16000, temperature=1, top_p=0.95, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") Enterprise
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