모델 정보

NVIDIA-Nemotron-3-Nano-4B

unsloth · NVIDIA-Nemotron-3-Nano-4B

NVIDIA-Nemotron-3-Nano-4B-BF16 is a small language model (SLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model has been compressed from NVIDIA-Nemotron-Nano-9B-v2 using the Nemotron Elastic framework. The details of the parent model NVIDIA-Nemotron-Nano-9B-v2 can be found in (Nemotron-H tech report). The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers. NVIDIA-Nemotron-3-Nano-4B is an edge-ready small language model intended for Agentic AI in edge platforms (Jetson Thor, GeForce RTX, DGX Spark). It targets key-uses including AI gaming NPCs (teammates / companions), local voice assistants (for devices, apps, and games), and IoT automation. It is to be used in English and coding languages.

번역 대기 중입니다. 원문을 표시합니다.

텍스트 생성license:other
컨텍스트 창미공개
입력 → 출력텍스트 → 텍스트
모델 웨이트미확인
정보 업데이트2026/10/30 15:57:46
OVERVIEW

모델 소개

NVIDIA-Nemotron-3-Nano-4B-BF16 is a small language model (SLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model has been compressed from NVIDIA-Nemotron-Nano-9B-v2 using the Nemotron Elastic framework. The details of the parent model NVIDIA-Nemotron-Nano-9B-v2 can be found in (Nemotron-H tech report). The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers. NVIDIA-Nemotron-3-Nano-4B is an edge-ready small language model intended for Agentic AI in edge platforms (Jetson Thor, GeForce RTX, DGX Spark). It targets key-uses including AI gaming NPCs (teammates / companions), local voice assistants (for devices, apps, and games), and IoT automation. It is to be used in English and coding languages.

주요 특징

特点摘要待依据来源补齐,不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

PRICING & COST

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

출처 / 채널입력출력과금 단위수집 시간
확인된 요금이 없습니다. 출처를 확인하세요.

월간 기본 토큰 비용 추정

—

추정치에는 캐시 할인, 구간 요금, 멀티모달 추가 요금, 검색, 세금 및 추가 추론 출력이 포함되지 않습니다.

INDEPENDENT BENCHMARKS

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

등록된 전체 순위 보기 →

자주 묻는 질문

오픈 웨이트면 무료 상업 이용이 가능한가요?

반드시 그렇지는 않습니다. 버전별 라이선스, 상업 이용 조건과 정책을 확인하세요. 자체 배포에는 연산 및 유지 비용도 듭니다.

실시간 결제 요금인가요?

수집 시간이 기록된 요금 스냅샷입니다. 최신 버전, 지역, 할인과 과금 방식은 해당 채널에서 확인하세요.

출처 및 추적 기록

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。