WaveCut · text

Nanbeige4.2-3B-heretic

WaveCut/Nanbeige4.2-3B-heretic

Nanbeige4.2-3B-heretic at Q4_K_M is exactly 2,574,808,288 bytes (2.40 GiB / 2.57 GB) — an effective 4.940 bits per weight, not the nominal 4. Its KV cache at 32K is 2.75 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
4.2B
Architecture
nanbeige
22 layers
Context
262,144
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q4_K_M2.40 GiB2,574,808,2884.940WaveCut
Q5_K_M2.78 GiB2,986,996,9605.731WaveCut
Q6_K3.19 GiB3,424,947,4246.571WaveCut
Q8_04.13 GiB4,434,787,2968.508WaveCut

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.34 GiB0.34 GiB22 / 0 / 0
8,1920.69 GiB0.69 GiB22 / 0 / 0
16,3841.38 GiB1.38 GiB22 / 0 / 0
32,7682.75 GiB2.75 GiB22 / 0 / 0
65,5365.50 GiB5.50 GiB22 / 0 / 0
131,07211.00 GiB11.00 GiB22 / 0 / 0

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 2.18 GiB. The real file is 2.40 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
22
Attention heads
48
KV heads
8
Head dim
128
Hidden size
3072
Vocab
166,144
Sliding window
none
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Nanbeige4.2-3B-heretic need?
Q4_K_M is exactly 2,574,808,288 bytes (2.40 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Nanbeige4.2-3B-heretic's KV cache?
2.75 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of Nanbeige4.2-3B-heretic 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.