---
title: "Abhinav Khattar — author profile and papers · Reasoning"
description: "Abhinav Khattar, Nvidia: 3 papers on Elamur Research (2025), h-index 5 — affiliations, topics, co-authors and who cites them in our library."
canonical: "https://research.elamur.ai/free/en/authors/abhinav-khattar/"
---
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# Abhinav Khattar

Abhinav Khattar has 3 papers in our library, in 2025, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Akhiad Bercovich](https://md.research.elamur.ai/free/en/authors/akhiad-bercovich/), [Aleksander Ficek](https://md.research.elamur.ai/free/en/authors/aleksander-ficek/), [Aarti Basant](https://md.research.elamur.ai/free/en/authors/aarti-basant/).

3 papers in our library · 2025

[OpenAlex](https://openalex.org/A5013604364) · [**h-index 5**](https://openalex.org/authors/A5013604364) · [21 works](https://openalex.org/works?filter=author.id:A5013604364) · [107 citations](https://openalex.org/works?filter=author.id:A5013604364&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/abhinav-khattar/#papers) · [🔗 Cited 179 times in our library](https://md.research.elamur.ai/free/en/authors/abhinav-khattar/cited-by/) · [⭐ Paper Stars ranking: #6760](https://md.research.elamur.ai/free/en/paper-stars/page/68/#star-6760)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Abhinav+Khattar) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Abhinav+Khattar) · [dblp](https://dblp.org/search?q=Abhinav+Khattar) · [ACL Anthology](https://aclanthology.org/search/?q=Abhinav+Khattar)

## Affiliations

[🏢**Nvidia** Reading · United Kingdom 2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/companies/nvidia-united-kingdom/) · [🏢**Nvidia** Santa Clara · United States 2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/companies/nvidia/) · [🎓**Indraprastha Institute of Information Technology Delhi** Delhi · India 2018–2019 · 6 papers](https://md.research.elamur.ai/free/en/universities/indraprastha-institute-of-information-technology-delhi/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/)

🧭 Topics 2 Research subjects and languages the work is about.[Reasoning · 2 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/)

## Works by year

### 2020s

[**2025** 3 papers](https://md.research.elamur.ai/free/en/authors/abhinav-khattar/year/2025/)

## Papers

The most cited first (OpenAlex and our library).

1. **[NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://md.research.elamur.ai/free/en/papers/basant-2025-nvidia-nemotron-nano-2-accurate/)** *(2025 · 39 citations)* Reasoning models spend most of their compute producing long chains of thought. In standard Transformers, the attention KV cache makes such long generations slow and memory-hungry. NVIDIA addresses this with Nemotron-Nano-9B-v2, which uses the Nemotron-H hybrid design: most self-attention layers are replaced by…
2. **[Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-h-family-accurate-efficient/)** *(2025 · 38 citations)* Self-attention costs grow with sequence length, both in compute per generated token and in KV-cache memory.
3. **[Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-3-nano-open-efficient/)** *(2025 · 27 citations)* NVIDIA presents Nemotron 3 Nano 30B-A3B, an open language model for agentic use, reasoning and chat that aims for a better balance between accuracy and inference speed.

## Co-authors here

9 co-authors with a page, the most papers together first.

- [Akhiad Bercovich](https://md.research.elamur.ai/free/en/authors/akhiad-bercovich/) ×3
- [Aleksander Ficek](https://md.research.elamur.ai/free/en/authors/aleksander-ficek/) ×3
- [Aarti Basant](https://md.research.elamur.ai/free/en/authors/aarti-basant/) ×2
- [Adithya Renduchintala](https://md.research.elamur.ai/free/en/authors/adithya-renduchintala/) ×2
- [Alex Kondratenko](https://md.research.elamur.ai/free/en/authors/alex-kondratenko/) ×2
- [NVIDIA: Aaron Blakeman](https://md.research.elamur.ai/free/en/authors/nvidia-aaron-blakeman/) ×2
- [Aaron Grattafiori](https://md.research.elamur.ai/free/en/authors/aaron-grattafiori/)
- [Ameya Sunil Mahabaleshwarkar](https://md.research.elamur.ai/free/en/authors/ameya-sunil-mahabaleshwarkar/)
- [Andrew Tao](https://md.research.elamur.ai/free/en/authors/andrew-tao/)

## Cited by

Papers of our library cite their works 179 times (of the 149,439 papers whose bibliographies we read); [40 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/abhinav-khattar/cited-by/).

Updated 30 October 2026
