Source: Ollama
NVIDIA's enterprise-grade LLM family, deployed privately on your infrastructure via Ollama. No telemetry. No NGC account required. No data leaving your environment.
Nemotron is NVIDIA's family of large language models, built on top of Llama base weights and fine-tuned using NVIDIA's proprietary alignment and synthetic data pipelines. The models are released under open licenses and are fully runnable via Ollama, making them accessible for private enterprise deployments without requiring NVIDIA cloud access, an NGC account, or any NVIDIA-managed inference endpoint.
The flagship model, Nemotron-4 340B, was trained on 9 trillion tokens and achieves benchmark scores that rival or exceed GPT-4 on a range of tasks including reasoning, code generation, and mathematical problem solving. Smaller variants — including the Nemotron Mini 4B — are optimized for edge deployment and resource-constrained environments.
For organizations that already operate NVIDIA GPU infrastructure — whether in a private data center, a co-location facility, or on GPU-equipped cloud instances — Nemotron offers a direct path to GPT-4-class performance without touching any cloud AI service or exposing sensitive queries to external APIs.
Privacy context: NVIDIA offers hosted inference through its NIM (NVIDIA Inference Microservices) platform. While NIM is technically designed for on-premise deployment, organizations using NVIDIA's cloud API endpoints are subject to NVIDIA's data handling policies. Running Nemotron locally via Ollama eliminates this surface entirely — inference happens on hardware you control, with no outbound connections to NVIDIA services.
Compact model optimized for instruction following and on-device deployment. Fits in 4GB VRAM. Suitable for edge servers, Raspberry Pi clusters, and lightweight enterprise chatbots where GPU memory is limited.
Mid-range variant balancing capability and resource efficiency. Strong performance on reasoning tasks with 8-16GB VRAM requirements. The practical choice for most enterprise inference servers running on Lightsail or small on-premise GPU nodes.
NVIDIA's flagship open model. GPT-4 competitive on reasoning, code, and math. Requires multi-GPU deployment — typically 4x A100 80GB or equivalent. The reference model for organizations needing maximum capability with full data sovereignty.
NVIDIA's alignment fine-tune of Meta's Llama 3.1 70B. Particularly strong on helpfulness benchmarks. Runs on a single A100 80GB or two A6000 48GB GPUs. The most balanced option for enterprise deployments needing high capability without 340B-scale hardware.
Nemotron targets enterprise GPU infrastructure, but the Nemotron Mini 4B is genuinely accessible — it fits in 4 GB and runs a capable private assistant on a single consumer GPU. For an enthusiast already running NVIDIA hardware, it is a clean, telemetry-free local model. The larger variants are out of reach for typical personal hardware.
Organizations already operating NVIDIA GPUs get a natural upgrade path: Llama-3.1-Nemotron-70B on a single A100 covers most enterprise assistant, summarization, and analysis work, and the NVIDIA Open Model License permits commercial use. For regulated teams, running it via Ollama removes the NGC account and NIM telemetry surface entirely.
Nemotron's NVIDIA alignment pipeline produces strong code generation and reliable multi-step instruction following, which suits internal developer tools and structured-output automation. Ollama's single-binary deployment avoids NIM's CUDA-version and container complexity, so teams without dedicated MLOps can stand up a private endpoint quickly and keep proprietary code in-house.
Over 98% of Nemotron-4's alignment data was synthetically generated, and NVIDIA open-sourced both the pipeline and a companion Reward model. For research teams, that makes Nemotron useful not just for analysis but as a tool for generating and scoring synthetic training data to fine-tune smaller domain models — entirely on-premise.
NVIDIA's NIM platform is positioned as an on-premise solution, and in many configurations it does run locally. However, organizations evaluating Nemotron for sensitive workloads encounter several concerns that make a pure Ollama-based deployment preferable:
NIM containers include telemetry components that report usage metrics back to NVIDIA. While NVIDIA states this telemetry does not include inference content, organizations subject to strict data governance requirements — healthcare, defense contracting, financial services — may find even usage telemetry unacceptable. Ollama runs with no such components.
NIM deployment requires an active NVIDIA GPU Cloud account, which creates an account-level dependency on NVIDIA infrastructure. If your organization needs to operate in an air-gapped environment or has policies against external service accounts for production AI systems, NIM introduces a compliance friction point. Ollama requires no accounts, no API keys, and no external service dependencies.
NIM uses complex containerization with specific NVIDIA driver and CUDA version requirements. Ollama abstracts this entirely, handling model quantization, memory management, and inference serving with a single binary and a one-line pull command. For teams without dedicated MLOps resources, Ollama-based Nemotron deployment is dramatically simpler to maintain.
Nemotron models are released under NVIDIA's Open Model License, which permits commercial use but includes restrictions on redistribution and derivative model training. Review the license terms before production deployment. The license does not restrict private inference use.
| Model | VRAM Required | Recommended Hardware | Throughput |
|---|---|---|---|
| Nemotron Mini 4B | 4GB | AWS Lightsail 4GB / RTX 3060 | ~45 tokens/sec |
| Nemotron 12B (Q4) | 8GB | AWS Lightsail 8GB / RTX 3080 | ~22 tokens/sec |
| Nemotron-70B (Q4) | 40GB | A100 40GB / 2x RTX 3090 | ~12 tokens/sec |
| Nemotron-4 340B (Q4) | 180GB+ | 4x A100 80GB / 8x H100 | ~4 tokens/sec |
Code review, architecture documentation, and technical specification drafting using a model that never sends your proprietary codebase to an external service.
Air-gapped deployment on existing NVIDIA GPU infrastructure. Nemotron's NVIDIA provenance provides institutional familiarity for procurement teams already operating NVIDIA hardware.
Risk analysis, regulatory document summarization, and internal research synthesis on sensitive financial data that cannot leave the organization's network boundary.
Clinical note processing, research literature review, and protocol drafting under HIPAA constraints. On-premise Nemotron eliminates the BAA negotiation process entirely.
Contract analysis and due diligence on M&A documents using a model running inside the firm's existing IT perimeter, with no outside counsel data exposure concerns.
Process documentation, quality control analysis, and technical training material generation on edge servers co-located with production facilities.
Nemotron-4 340B outperforms Llama 3.1 70B on most reasoning benchmarks while sharing a similar deployment profile for the 70B variant. For organizations already invested in NVIDIA GPU infrastructure, Nemotron provides a natural upgrade path from smaller models without changing the underlying hardware stack.
Against hosted APIs like GPT-4, the comparison is not primarily about benchmark scores — it is about data control. A Nemotron 70B deployment running on a single A100 in your data center processes queries that never leave your environment, cannot be logged by a third party, and are not subject to any provider's terms of service changes. For regulated industries, this is not a nice-to-have. It is often a compliance requirement.
Against Mixtral 8x22B, Nemotron's NVIDIA-optimized training pipeline produces stronger results on code and mathematics, while Mixtral maintains advantages in multilingual tasks and raw throughput on equivalent hardware due to the Mixture-of-Experts architecture.
Nemotron-4 and the Llama-Nemotron alignment fine-tunes are released under the NVIDIA Open Model License, which NVIDIA confirms is commercially usable, permits derivative models, and does not claim ownership of generated outputs. The one nuance: models built on Meta's Llama (such as Llama-3.1-Nemotron-70B) are additionally governed by the Llama Community License and carry the "Built with Llama" attribution requirement. Review the applicable terms before commercial deployment.
| Model | License | Commercial use | Model card |
|---|---|---|---|
| Nemotron-4 340B Instruct | NVIDIA Open Model License | Permitted | nvidia/Nemotron-4-340B-Instruct → |
| Llama-3.1-Nemotron-70B Instruct | NVIDIA Open Model License + Llama 3.1 Community License | Permitted, attribution required | nvidia/Llama-3.1-Nemotron-70B-Instruct → |
| Nemotron Mini 4B Instruct | NVIDIA Open Model License | Permitted | nvidia/Nemotron-Mini-4B-Instruct → |
The NVIDIA Open Model License permits commercial use; Llama-based variants additionally require compliance with the Llama Community License and "Built with Llama" attribution. License information sourced from Hugging Face model cards and may change between releases. Always verify current terms before commercial deployment. NoCloudGPT is not a legal advisor.
The Nemotron family spans edge-friendly to multi-GPU flagship sizes. NoCloudGPT will confirm current model availability, license terms, tags, and hardware fit before deployment begins. NoCloudGPT confirms hardware requirements and selects the right quantization before your pilot begins.
| Model | Deployment tier | Best for | |
|---|---|---|---|
| Nemotron Mini 4B | Standard AI — 4 GB | Edge servers, lightweight private assistants | Get quote → |
| Nemotron 12B (Q4) | Standard AI — 8 GB | Most enterprise inference servers | Get quote → |
| Llama-3.1-Nemotron-70B (Q4) | Heavy AI — single A100 / 2× A6000 | Balanced high-capability enterprise deployment | Get quote → |
| Nemotron-4 340B (Q4) | Enterprise AI — 4× A100 80GB+ | Maximum capability with full data sovereignty | Get quote → |
NoCloudGPT provides managed deployment of Nemotron models across three infrastructure tiers:
Fully configured AWS Lightsail instances with Ollama, Nemotron, and OpenWebUI pre-installed. Suitable for teams of 5-50 users. Monthly flat-rate pricing. No GPU required for 4B and quantized 12B variants.
Single A100 or equivalent deployment on AWS, Lambda Labs, or CoreWeave. Full private networking configuration, OpenWebUI with authentication, and optional integration with your existing SSO provider.
We travel to your data center or configure remote deployment on your existing NVIDIA infrastructure. Includes Ollama cluster configuration, load balancing, and OpenWebUI enterprise setup with LDAP/Active Directory integration.
Request Deployment QuoteArtwork and source references
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Developer reference
Nemotron is developed by NVIDIA. NoCloudGPT is an independent deployment service not affiliated with NVIDIA.
Model catalog
See Ollama library: nemotron for current tags and sizes.