NVIDIALlama 3_3 Nemotron Super 49B V1
NVIDIA's Llama 3_3 Nemotron Super 49B V 1 is a large language model and chat model that excels at balancing model accuracy and efficiency, making it suitable for various tasks such as reasoning, human chat preferences, and tool calling. Its notable technical trait is the use of a novel Neural Architecture Search (NAS) approach, which reduces the model's memory footprint and enables larger workloads.
Input
Output
Context
131K
Max Output
4K
Parameters
49.9B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for Llama 3_3 Nemotron Super 49B V1
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 Llama 3_3 Nemotron Super 49B V1
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama 3_3 Nemotron Super 49B V1 advertises a context window of 131,072 tokens, with a maximum output of 4,096 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Llama 3_3 Nemotron Super 49B V1 is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Llama 3_3 Nemotron Super 49B V1 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.
Llama 3_3 Nemotron Super 49B V1 has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Llama 3_3 Nemotron Super 49B V1 is shown as not available rather than estimated.
Not on the managed pool today; Llama 3_3 Nemotron Super 49B V1 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 48.8 GB at INT8 for the weights and a default context, from the catalog's 49.9B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 16, 2026
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