Building a listed tool
Mukundhan Srinivasan
Named behind 10 listings on Anchor Terminal.
What Mukundhan builds
Nemotron 3.5 ASR Streaming 0.6B C · Speech-to-text
Nemotron 3.5 ASR Streaming 0.6B is NVIDIA's open-weight streaming speech recognition model, a 600M-parameter FastConformer with an RNN-T decoder, transcribing 32 language-locales with punctuation and an optional detected-language tag at chunk sizes from 80 ms to 1.12 s, under OpenMDW-1.1.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
NVIDIA Nemotron 3.5 Bolt E4B 4GB C · Open-weight models
Nemotron 3.5 Bolt E4B 4GB is NVIDIA's open-weight small language model for local voice assistants, interactive characters and device agents, post-trained from Google's Gemma 4 E4B, with 8B parameters (4B effective) and a 64K-token context.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3.5 Lightning 30B A3B B · Open-weight models
Nemotron 3.5 Lightning is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 30B parameters with 3B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3.5 Parse B · Document parsing & extraction
Nemotron 3.5 Parse is NVIDIA's open-weight document parser, 0.63B parameters, that turns a page image into text with layout classes, bounding boxes and reading order, and renders Markdown, HTML tables and JSON, under OpenMDW-1.1.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3.5 Super VL C · Model APIs & inference
Nemotron 3.5 Super VL is NVIDIA's open-weight vision-language model for agents, a 120B mixture of experts with 12B active parameters that reads text, images and video. NVIDIA hosts a free trial endpoint, and the weights are under OpenMDW-1.1.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3 Nano 30B A3B B · Open-weight models
Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3 Nano 4B C · Open-weight models
Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3 Nano Omni 30B A3B Reasoning C · Open-weight models
Nemotron 3 Nano Omni is NVIDIA's open-weight multimodal model that takes video, audio, images and text and returns text, a hybrid Mamba-Transformer mixture of experts with about 31B parameters and 3B active, published in BF16, FP8 and NVFP4.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3 Super 120B A12B B · Open-weight models
Nemotron 3 Super is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 120B parameters with 12B active, published on Hugging Face in BF16, FP8 and NVFP4 under the NVIDIA Nemotron Open Model License.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Nemotron 3 Ultra 550B A55B B · Open-weight models
Nemotron 3 Ultra is NVIDIA's largest open-weight hybrid Mamba-2 and latent mixture-of-experts reasoning model for agents, 550B parameters with 55B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.
Global Head, Nemotron and Post Training, researched 27 October 2026 · the people behind it
Works with
- Chris Malachowsky Co-founder, NVIDIA
- Curtis Priem Co-founder, NVIDIA
- Ganesh K. System Software Manager; NeMo Guardrails product strategy and engineering lead
- Jensen Huang Co-founder, NVIDIA
- Nithin Rao Koluguri Senior Research Scientist; Parakeet-TDT-0.6B-v3 paper co-author
- Sean Sodha Senior Software Product Manager, Nemotron RAG & Generative AI
Questions
Who is Mukundhan Srinivasan?
Mukundhan Srinivasan is global Head at Nemotron and Post Training.
What does NVIDIA make?
Anchor Terminal lists Nemotron 3.5 ASR Streaming 0.6B, NVIDIA Nemotron 3.5 Bolt E4B 4GB, Nemotron 3.5 Lightning 30B A3B, Nemotron 3.5 Parse, Nemotron 3.5 Super VL, Nemotron 3 Nano 30B A3B, Nemotron 3 Nano 4B, Nemotron 3 Nano Omni 30B A3B Reasoning, Nemotron 3 Super 120B A12B and Nemotron 3 Ultra 550B A55B from NVIDIA. Nemotron 3.5 ASR Streaming 0.6B is NVIDIA's open-weight streaming speech recognition model, a 600M-parameter FastConformer with an RNN-T decoder, transcribing 32 language-locales with punctuation and an optional detected-language tag at chunk sizes from 80 ms to 1.12 s, under OpenMDW-1.1.
How are Mukundhan's tools graded?
Nemotron 3.5 ASR Streaming 0.6B is graded C, 59.8 out of 100; NVIDIA Nemotron 3.5 Bolt E4B 4GB is graded C, 61 out of 100; Nemotron 3.5 Lightning 30B A3B is graded B, 64.2 out of 100; Nemotron 3.5 Parse is graded B, 66.5 out of 100; Nemotron 3.5 Super VL is graded C, 59 out of 100; Nemotron 3 Nano 30B A3B is graded B, 67.1 out of 100; Nemotron 3 Nano 4B is graded C, 56.8 out of 100; Nemotron 3 Nano Omni 30B A3B Reasoning is graded C, 60.7 out of 100; Nemotron 3 Super 120B A12B is graded B, 66.2 out of 100; Nemotron 3 Ultra 550B A55B is graded B, 68.8 out of 100, on Anchor Terminal's benchmark, which scores every listing against the same checklist.
Sources (8)
- https://www.linkedin.com/in/mukundhansrinivasan/
- https://developer.nvidia.com/nemotron
- https://www.linkedin.com/in/sean-sodha/details/experience/
- https://huggingface.co/blog/nvidia/nemotron-retriever-world-class-agentic-retrieval
- https://www.linkedin.com/in/sean-sodha
- https://huggingface.co/nvidia/Nemotron-3.5-Parse
- https://www.nvidia.com/en-gb/
- https://www.nvidia.com/en-gb/contact/
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