NVIDIA
NVIDIA
Language model · United States ·Nov 2024

Hymba

Language Open (Restricted)
1.5B
Parameters

About Hymba

Hymba is an AI model developed by NVIDIA, in the language category, released in 2024, made available as an open-weights model with 1.5B params.

On this page you'll find Hymba's full specifications. Review provider pricing and benchmark scores below, or compare Hymba head-to-head with other language models.

→ More models by NVIDIA → All language models → AI benchmarks leaderboard

Links

Announcement ↗ Hugging Face ↗

Run Hymba locally — what to buy

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Sized to what Hymba actually needs (~8k context at Q4), not to the biggest GPU: Good = cheapest that runs it, Better = best value, Best = most headroom. Speed is a hardware estimate; anything past ~120 tok/s is shown as instant.

Best
Intel Arc B580 12GB
Consumer GPU · ~$249
~120 tok/s
Q4 · fits full context · up to 8k ctx
Runs with: ExLlamaV2, KoboldCpp, LM Studio
See full estimate →

Software support

·ExLlamaV2 ·KoboldCpp ·LM Studio ·MLX ·Ollama ·SGLang ·TensorRT-LLM ·llama.cpp ·vLLM ?KTransformers ?text-generation-webui

✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.

Consumer GPU
116–120+ tok/s
33 options →
Apple
116–120+ tok/s
18 options →
Datacenter GPU
120+ tok/s
13 options →

Community

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Frequently asked questions

What is Hymba? ▶

Hymba is an AI model developed by NVIDIA, in the language category, released in 2024. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created Hymba? ▶

Hymba was developed by NVIDIA and released in 2024.

Is Hymba open source or proprietary? ▶

Hymba ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.

How much does Hymba cost? ▶

Pricing for Hymba depends on the provider. See the providers table on this page for the latest API rates.

How does Hymba perform on benchmarks? ▶

Benchmark scores for Hymba are listed on this page as they are published. You can compare it head-to-head with other models on the GenAIList compare tool.

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