Nemotron H 4B Instruct 128K
NVIDIA's Nemotron H 4B Instruct 128K is a large language model optimized for chat, instruction following, and tool-calling use-cases, with a hybrid architecture combining Mamba-2, MLP, and Attention layers. It is particularly suited for tasks that require understanding and responding to instructions, as well as generating human-like text in single and multi-turn conversations.
Input
Output
Context
128K
Max Output
8K
Parameters
4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Resources & Links
Estimated GPU requirements for Nemotron H 4B Instruct 128K
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 Nemotron H 4B Instruct 128K
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Nemotron H 4B Instruct 128K advertises a context window of 128,000 tokens, with a maximum output of 8,192 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. Nemotron H 4B Instruct 128K is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Nemotron H 4B Instruct 128K 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 Nemotron H 4B Instruct 128K 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; Nemotron H 4B Instruct 128K 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 4.3 GB at INT8 for the weights and a default context, from the catalog's 4B 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: Aug 28, 2026
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