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magnum-v4-9b

anthracite-org/magnum-v4-9b

magnum-v4-9b at Q4_K_M is exactly 5,761,057,696 bytes (5.37 GiB / 5.76 GB) — an effective 4.987 bits per weight, not the nominal 4. Its KV cache at 32K is 5.99 GiB, not the 10.50 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
9.2B
Architecture
gemma2
42 layers
Context
8,192
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
IQ2_M3.20 GiB3,434,668,9602.973bartowski
Q2_K3.54 GiB3,805,397,9203.294bartowski
Q2_K_L3.75 GiB4,027,605,9203.486bartowski
IQ3_XS3.86 GiB4,144,989,0883.588bartowski
Q3_K_S4.04 GiB4,337,664,9283.755bartowski
IQ3_M4.19 GiB4,494,615,4563.891bartowski
Q3_K_M4.43 GiB4,761,781,1524.122bartowski
Q3_K_L4.78 GiB5,132,452,7684.443bartowski
IQ4_XS4.83 GiB5,183,030,1764.487bartowski
Q4_05.08 GiB5,459,198,8804.726bartowski
Q4_K_S5.10 GiB5,478,925,2164.743bartowski
Q4_K_M5.37 GiB5,761,057,6964.987bartowski
Q4_K_L5.57 GiB5,983,265,6965.179bartowski
Q5_K_S6.04 GiB6,483,592,0965.612bartowski
Q5_K_M6.19 GiB6,647,366,5605.754bartowski
Q5_K_L6.40 GiB6,869,574,5605.947bartowski
Q6_K7.07 GiB7,589,069,7286.569bartowski
Q6_K_L7.27 GiB7,811,277,7286.762bartowski
Q8_09.15 GiB9,827,148,7048.507bartowski
F1617.22 GiB18,490,679,96816.006bartowski

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0961.31 GiB1.31 GiB21 / 21 / 0
8,1922.05 GiB2.63 GiB1.28×21 / 21 / 0
16,3843.36 GiB5.25 GiB1.56×21 / 21 / 0
32,7685.99 GiB10.50 GiB1.75×21 / 21 / 0
65,53611.24 GiB21.00 GiB1.87×21 / 21 / 0
131,07221.74 GiB42.00 GiB1.93×21 / 21 / 0

21 of 42 layers cache only a 4,096-token window rather than the full context, on a period of 2. Figures assume the default configuration; --swa-full disables the saving entirely.

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 4.84 GiB. The real file is 5.37 GiB, because a quantization is a mixture and some tensors are always kept at higher precision. The larger discrepancy is the cache: a flat formula gives 10.50 GiB at 32K context where the real figure is 5.99 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
42
Attention heads
16
KV heads
8
Head dim
256
Hidden size
3584
Vocab
256,000
Sliding window
4096
SWA period
2
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does magnum-v4-9b need?
Q4_K_M is exactly 5,761,057,696 bytes (5.37 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is magnum-v4-9b's KV cache?
5.99 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 magnum-v4-9b 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.