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Nemotron 3 Super Launches on Bedrock

Nemotron 3 Super Launches on Bedrock
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๐Ÿ’กNVIDIA's powerful Nemotron 3 Super on Bedrock: specs, use cases, quickstart guide.

โšก 30-Second TL;DR

What Changed

NVIDIA Nemotron 3 Super now available via Amazon Bedrock

Why It Matters

Brings NVIDIA's advanced LLM to AWS users without self-hosting, speeding up GenAI prototyping and deployment on scalable Bedrock infrastructure.

What To Do Next

Log into Amazon Bedrock console and test Nemotron 3 Super model inference via the playground.

Who should care:Developers & AI Engineers

Key Points

  • โ€ขNVIDIA Nemotron 3 Super now available via Amazon Bedrock
  • โ€ขDetails technical specs of the high-performance LLM
  • โ€ขOutlines generative AI application use cases
  • โ€ขIncludes step-by-step setup guide for Bedrock users

๐Ÿง  Deep Insight

Web-grounded analysis with 8 cited sources.

๐Ÿ”‘ Enhanced Key Takeaways

  • โ€ขNemotron 3 Super is a 12B active / 120B total parameter hybrid Mixture-of-Experts (MoE) model with Mamba-Transformer architecture optimized for multi-agent applications like reasoning and tool calling[1][2][3].
  • โ€ขIt achieves up to 2.2x higher inference throughput than GPT-OSS-120B and 7.5x higher than Qwen3.5-122B on 8k input/16k output benchmarks, while supporting up to 1M token context length[2][7].
  • โ€ขThe model incorporates novel technologies including LatentMoE for accuracy, MTP layers for speculative decoding, and NVFP4 pretraining for 4x faster inference on NVIDIA B200 GPUs[2][5][6].
  • โ€ขFully open-source with weights, data, and recipes available, accessible via Hugging Face, NVIDIA NGC, NIM, and hosted platforms like Together AI[3][4].
๐Ÿ“Š Competitor Analysisโ–ธ Show
FeatureNemotron 3 SuperGPT-OSS-120BQwen3.5-122B
Parameters120B total (12B active MoE)120B122B
ArchitectureHybrid Mamba-Transformer MoETransformerMoE
Throughput (8k in/16k out)Baseline2.2x slower7.5x slower
Context Length1M tokens<1M (outperforms on RULER)<1M (outperforms on RULER)
BenchmarksLeading on GPQA Diamond, AIME 2025, LiveCodeBenchComparable/lowerComparable/lower

๐Ÿ› ๏ธ Technical Deep Dive

  • Architecture: Hybrid Mixture-of-Experts (MoE) with Mamba-Transformer backbone; 120B total parameters, 12B activated per forward pass via sparse MoE routing; includes LatentMoE (hardware-aware experts), MTP (Multi-Token Prediction) layers for speculative decoding[2][3][5][7].
  • Pretraining: NVFP4 format on 25-trillion-token corpus; optimized for NVIDIA Blackwell GPUs, 4x inference speedup on B200 vs FP8 on H100[2][6][7].
  • Inference Configs: Max model length 65,536 tokens; tensor parallel size 2-4; 90% GPU memory utilization; KV cache auto/FP8; FLASH_ATTN backend; vLLM or TRT-LLM serving on 8x B200-SXM[1][7].
  • Memory Estimates: FP16 ~240GB VRAM; 4-bit quantized ~60-80GB (multi-GPU/A100/H100 required); supports QLoRA fine-tuning via NVIDIA NeMo[4].

๐Ÿ”ฎ Future ImplicationsAI analysis grounded in cited sources

Nemotron 3 Super enables single-GPU multi-agent deployments
Its MoE design with 12B active parameters and high throughput optimizes for running multiple collaborating agents on one GPU, reducing costs for agentic applications[3].
NVFP4 pretraining sets standard for Blackwell efficiency
Native NVFP4 cuts memory and speeds inference 4x on B200 GPUs versus prior formats, accelerating adoption of next-gen NVIDIA hardware[6].
Open-source release boosts enterprise customization
Full openness of weights, data, and recipes via Hugging Face/NGC allows fine-tuning with QLoRA for domain-specific tasks on A100/H100 clusters[4].

โณ Timeline

2025-12
Nemotron 3 Nano released as first in Nemotron 3 series with hybrid MoE architecture
2026-03
Nemotron 3 Super released: 120B MoE model with LatentMoE, MTP, NVFP4 for agentic reasoning
2026-03
Nemotron 3 Super launched on Amazon Bedrock for generative AI use cases
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Original source: AWS Machine Learning Blog โ†—