chore(card): add hardware compatibility section
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README.md
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license: apache-2.0
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base_model: mistralai/Voxtral-4B-TTS-2603
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tags:
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- rotorquant
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- kv-cache-quantization
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- voxtral
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- mistral
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- tts
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- voice-cloning
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- zero-shot
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- quantized
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library_name: transformers
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pipeline_tag: text-to-speech
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language:
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- en
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inference: false
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---
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# Voxtral-4B-TTS-2603-RotorQuant
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This is a **documentation repository** that explains how to combine Voxtral-4B-TTS-2603's weights with RotorQuant inference-time KV cache compression. No weights are stored here — use the base model directly and apply RotorQuant via the Python package or llama.cpp fork.
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## What is this?
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KV cache compression reduces the memory used by the attention cache during inference. Unlike weight quantization (which is baked into the GGUF/MLX file), KV cache compression is applied at runtime — so the same base weights can be used with or without compression.
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license: apache-2.0
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base_model: mistralai/Voxtral-4B-TTS-2603
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tags:
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- rotorquant
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- kv-cache-quantization
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- voxtral
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- mistral
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- tts
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- voice-cloning
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- zero-shot
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- quantized
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library_name: transformers
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pipeline_tag: text-to-speech
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---
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# Voxtral-4B-TTS-2603-RotorQuant
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This is a **documentation repository** that explains how to combine Voxtral-4B-TTS-2603's weights with RotorQuant inference-time KV cache compression. No weights are stored here — use the base model directly and apply RotorQuant via the Python package or llama.cpp fork.
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## Hardware compatibility
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| Device | VRAM / RAM | Recommendation |
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| --- | --- | --- |
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| Any host that runs the base model | baseline + runtime savings | RotorQuant/TurboQuant is a KV-cache runtime modifier; pair with any weight variant |
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## What is this?
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KV cache compression reduces the memory used by the attention cache during inference. Unlike weight quantization (which is baked into the GGUF/MLX file), KV cache compression is applied at runtime — so the same base weights can be used with or without compression.
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