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microsoft/VibeVoice-ASR

20GB VRAM (FP16)
8.67B parametersBy microsoftReleased 2026-014,096 token context

Minimum VRAM

20GB

FP16 (full model) • Q4 option ≈ 5GB

Best Performance

Collecting data

Benchmark incoming

Most Affordable

Retail data pending

Waiting for retailers

Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.


Compatible GPUs

Filter by quantization, price, and VRAM to compare performance estimates.

We haven’t published GPU benchmarks for this model yet, but you can still plan a stable build:

  • Most modern GPUs with 12GB or more VRAM will run this model comfortably. Higher VRAM helps with Q8 and FP16 quantizations.
  • Pair that with 64GB system RAM and 100GB of fast storage for smooth inference.
  • Filter the GPU browser by at least 5GB of VRAM to see cards likely to fit while we verify benchmarks.
Browse GPUs with >=5GB VRAMView similar model guides
Don’t see your GPU? View all compatible hardware →

Detailed Specifications

Hardware requirements and model sizes at a glance.

Technical details

Parameters
8,674,021,857 (8.67B)
Architecture
vibevoice
Developer
microsoft
Released
January 2026
Context window
4,096 tokens

Quantization support

Q4
5GB VRAM required • 5GB download
Q8
10GB VRAM required • 10GB download
FP16
20GB VRAM required • 20GB download

Hardware Requirements

ComponentMinimumRecommendedOptimal
VRAM5GB (Q4)10GB (Q8)20GB (FP16)
RAM32GB64GB64GB
Disk50GB100GB-
Model size5GB (Q4)10GB (Q8)20GB (FP16)
CPUModern CPU (Ryzen 5/Intel i5 or better)Modern CPU (Ryzen 5/Intel i5 or better)Modern CPU (Ryzen 5/Intel i5 or better)

Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data


Frequently Asked Questions

Common questions about running microsoft/VibeVoice-ASR locally

What should I know before running microsoft/VibeVoice-ASR?

This model delivers strong local performance when paired with modern GPUs. Use the hardware guidance below to choose the right quantization tier for your build.

How do I deploy this model locally?

Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).

Which quantization should I choose?

Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.

What is the difference between Q4, Q4_K_M, Q5_K_M, and Q8 quantization for microsoft/VibeVoice-ASR?

Q4_K_M and Q5_K_M are GGUF quantization formats that balance quality and VRAM usage. Q4_K_M uses ~5GB VRAM with good quality retention. Q5_K_M uses slightly more VRAM but preserves more model accuracy. Q8 (~10GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for microsoft/VibeVoice-ASR.

Where can I download microsoft/VibeVoice-ASR?

Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.


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