ACE-Step · text

ACE-Step-v1-3.5B

ACE-Step/ACE-Step-v1-3.5B

ACE-Step-v1-3.5B at Q4_K_M is exactly 6,149,775,712 bytes (5.73 GiB / 6.15 GB) — an effective 14.883 bits per weight, not the nominal 4.

From the file· summed from 2 file(s)From the file· KV per layer
Parameters
3.3B
Architecture
pig
null layers
Context
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
BF160.42 GiB445,924,8961.079calcuis
Q2_K5.10 GiB5,478,716,57613.259calcuis
Q3_K_S5.27 GiB5,661,846,94413.702calcuis
Q3_K_M5.31 GiB5,705,264,54413.807calcuis
Q3_K_L5.35 GiB5,742,947,74413.899calcuis
Q4_K_S5.54 GiB5,946,381,12014.391calcuis
Q4_15.66 GiB6,075,460,73614.703calcuis
Q4_K_M2 shards5.73 GiB6,149,775,71214.883calcuis
Q5_K_S5.78 GiB6,207,817,15215.024calcuis
Q5_K_M5.82 GiB6,249,596,35215.125calcuis
Q5_15.90 GiB6,340,173,56815.344calcuis
Q6_K6.04 GiB6,489,074,56015.704calcuis
IQ4_XS6.22 GiB6,675,660,80016.156calcuis
IQ4_NL6.27 GiB6,734,643,20016.299calcuis
F164 shards7.34 GiB7,883,135,04019.078calcuis
Q4_03 shards12.01 GiB12,893,499,07231.203calcuis
Q5_02 shards12.27 GiB13,178,389,95231.893calcuis
F3212.31 GiB13,222,726,40032.000calcuis
Q8_02 shards13.67 GiB14,680,317,248calcuis

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 1.73 GiB. The real file is 5.73 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
Attention heads
20
KV heads
20
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 ACE-Step-v1-3.5B need?
Q4_K_M is exactly 6,149,775,712 bytes (5.73 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of ACE-Step-v1-3.5B 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.