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Nemotron-4 340B Instruct

Nemotron-4 340B Instruct is NVIDIA's 4K-context English chat model for synthetic-data generation and alignment under the NVIDIA Open Model License.

General Purpose Language ModelText Freemium
In plain English

What is this model and why does it matter?

Nemotron-4 340B Instruct is NVIDIA's large English chat model for synthetic-data generation, alignment and high-quality instruction following.

Synthetic data generationAlignment researchLarge-scale chatModel customizationEnterprise research
Model overview

Nemotron-4 340B Instruct: features, use cases and important details

Nemotron-4 340B Instruct is NVIDIA’s large aligned chat model built from the Nemotron-4 340B base model.

Verified model facts

NVIDIA documents a 4,096-token context window, June 2023 pretraining-data cutoff, English chat focus and commercial use under the NVIDIA Open Model License.

Current status

The official model remains downloadable and NVIDIA’s model card still links to build.nvidia.com for hosted experimentation.

Best fit

It is best for synthetic-data generation, alignment research and organizations building or customizing large language models.

Limitations

Its 340B size requires very large multi-GPU infrastructure, its context is short by current standards and its knowledge is static.

Get started

How to use this model

  1. Review the NVIDIA Open Model License.
  2. Download the official checkpoint or use NVIDIA's hosted build environment.
  3. Provision multi-GPU infrastructure for local inference.
  4. Use NVIDIA's recommended prompt format.
  5. Evaluate safety and synthetic-data quality before downstream training.
Copy and try

Example prompts

  • Generate high-quality synthetic instruction-response pairs for this domain.
  • Explain this complex technical topic for an expert audience.
  • Produce diverse training examples for this reasoning task.
Capabilities

What it can do

  • English chat
  • Synthetic data generation
  • Instruction following
  • Reasoning
  • Coding
  • Fine-tuning support
Best for

Practical use cases

  • Synthetic datasets
  • Alignment
  • Research
  • Custom LLM development
  • Large chat workloads
Pricing

What does it cost?

NVIDIA currently exposes a build.nvidia.com trial path; no stable per-token price is claimed for this checkpoint.

Simple summaryThe checkpoint is commercially usable but extremely expensive to self-host, with NVIDIA listing multi-H100/A100 hardware requirements.

What stands out

  • Commercially usable weights
  • Strong synthetic-data focus
  • Official NVIDIA deployment guidance
  • Customizable with NeMo

Things to consider

  • 340B model is extremely resource intensive
  • Only 4K context
  • English-focused
Limitations

Important restrictions and trade-offs

  • June 2023 knowledge cutoff
  • Very high serving cost
  • Can hallucinate or synthesize low-quality examples without filtering
SimplifyAITools verdict

Our editorial take

Best treated as a specialist NVIDIA research and synthetic-data model rather than a general-purpose modern long-context assistant.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗
  3. Open source 3 ↗
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