microsoft · audio tts

VibeVoice-1.5B

microsoft/VibeVoice-1.5B

VibeVoice-1.5B at Q4_K_M is exactly 3,536,105,184 bytes (3.29 GiB / 3.54 GB) — an effective 10.462 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
2.7B
Architecture
vibevoice-asr
null layers
Context
native (config.json)
License
mit

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q4_K1.76 GiB1,891,216,2885.595cstr
Q8_02.90 GiB3,115,973,5369.219cstr
Q2_K3.01 GiB3,229,028,0649.553gguf-org
IQ3_XXS3.07 GiB3,300,679,3929.765gguf-org
IQ3_S3.13 GiB3,362,094,8169.947gguf-org
Q3_K_M3.13 GiB3,362,094,8169.947gguf-org
IQ4_XS3.26 GiB3,495,161,56810.341gguf-org
IQ4_NL3.29 GiB3,536,105,18410.462gguf-org
Q4_K_M3.29 GiB3,536,105,18410.462gguf-org
Q4_03.29 GiB3,536,105,18410.462gguf-org
Q4_13.37 GiB3,617,992,41610.704gguf-org
Q5_03.45 GiB3,699,879,64810.946gguf-org
Q5_K_M3.45 GiB3,699,879,64810.946gguf-org
Q5_13.52 GiB3,781,766,88011.189gguf-org
Q6_K3.61 GiB3,873,890,01611.461gguf-org
Q8_03.90 GiB4,191,203,04012.400gguf-org
F165.04 GiB5,408,156,96016.000gguf-org
F165.04 GiB5,412,393,28016.013cstr
BF165.05 GiB5,419,511,52016.034gguf-org
F3210.07 GiB10,816,200,92832.000gguf-org

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 1.42 GiB. The real file is 3.29 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
Attention heads
KV heads
Head dim
Hidden size
Vocab
Sliding window
none
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does VibeVoice-1.5B need?
Q4_K_M is exactly 3,536,105,184 bytes (3.29 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of VibeVoice-1.5B should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.