KRX-Data · text

WON-Reasoning

KRX-Data/WON-Reasoning

WON-Reasoning at Q4_K_M is exactly 4,681,087,264 bytes (4.36 GiB / 4.68 GB) — an effective 4.919 bits per weight, not the nominal 4. Its KV cache at 32K is 1.75 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
7.6B
Architecture
qwen2
28 layers
Context
16,384
native (config.json)
License

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q2_K2.81 GiB3,014,288,9923.168mradermacher
Q3_K_S3.25 GiB3,490,572,0643.668mradermacher
Q3_K_M3.55 GiB3,806,594,8484.000mradermacher
Q3_K_L3.81 GiB4,086,662,9444.295mradermacher
IQ4_XS3.96 GiB4,248,356,8004.465mradermacher
Q4_K_S4.15 GiB4,455,782,6884.682mradermacher
Q4_K_M4.36 GiB4,681,087,2644.919mradermacher
Q5_K_S4.95 GiB5,313,011,4245.583mradermacher
Q5_K_M5.07 GiB5,442,666,2085.720mradermacher
Q6_K5.82 GiB6,251,843,8406.570mradermacher
Q8_07.54 GiB8,095,477,5688.507mradermacher
F1614.19 GiB15,232,124,28816.007mradermacher

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.22 GiB0.22 GiB28 / 0 / 0
8,1920.44 GiB0.44 GiB28 / 0 / 0
16,3840.88 GiB0.88 GiB28 / 0 / 0
32,7681.75 GiB1.75 GiB28 / 0 / 0
65,5363.50 GiB3.50 GiB28 / 0 / 0
131,0727.00 GiB7.00 GiB28 / 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 3.99 GiB. The real file is 4.36 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
28
Attention heads
28
KV heads
4
Head dim
128
Hidden size
3584
Vocab
151,665
Sliding window
none
SWA period
MLA
no
Experts
Experts per token
use_sliding_window
false

Questions people ask

How much VRAM does WON-Reasoning need?
Q4_K_M is exactly 4,681,087,264 bytes (4.36 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is WON-Reasoning's KV cache?
1.75 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 WON-Reasoning 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.