nvidia-llama-3-1-nemotron-70b-instruct
NVIDIA's llama-3.1-nemotron-70b-instruct is a chat model capable of processing long sequences of up to 16,384 tokens, allowing it to handle complex and extended conversations. It is genuinely best at tasks that require streaming, function calling, and generating structured output.
Input
Output
Context
16K
Max Output
4K
Parameters
70B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Resources & Links
Estimated GPU requirements for nvidia-llama-3-1-nemotron-70b-instruct
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about nvidia-llama-3-1-nemotron-70b-instruct
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
nvidia-llama-3-1-nemotron-70b-instruct advertises a context window of 16,384 tokens, with a maximum output of 4,096 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. nvidia-llama-3-1-nemotron-70b-instruct is an open-weight model released under the Llama3.1 license, so the weights can be downloaded and self-hosted within that license's terms.
nvidia-llama-3-1-nemotron-70b-instruct accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for nvidia-llama-3-1-nemotron-70b-instruct is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; nvidia-llama-3-1-nemotron-70b-instruct is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 68.4 GB at INT8 for the weights and a default context, from the catalog's 70B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 13, 2026
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