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Smilodon-9B-v1

Naphula/Smilodon-9B-v1

Smilodon-9B-v1 at Q4_K_M is exactly 5,761,061,408 bytes (5.37 GiB / 5.76 GB) — an effective 4.537 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
10.2B
Architecture
gemma2
42 layers
Context
8,192
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S2.22 GiB2,378,568,4801.873mradermacher
I1-IQ1_M2.37 GiB2,545,955,6162.005mradermacher
I1-IQ2_XXS2.63 GiB2,824,934,1762.224mradermacher
I1-IQ2_XS2.86 GiB3,067,384,6082.416mradermacher
I1-IQ2_S2.99 GiB3,211,490,0802.529mradermacher
I1-IQ2_M3.20 GiB3,434,672,9282.705mradermacher
I1-Q2_K_S3.31 GiB3,552,514,8482.797mradermacher
I1-IQ3_XXS3.54 GiB3,796,742,9442.990mradermacher
Q2_K3.54 GiB3,805,401,6322.997mradermacher
I1-Q2_K3.54 GiB3,805,401,8882.997mradermacher
I1-IQ3_XS3.86 GiB4,144,993,0563.264mradermacher
Q3_K_S4.04 GiB4,337,668,6403.416mradermacher
I1-IQ3_S4.04 GiB4,337,668,8963.416mradermacher
I1-Q3_K_S4.04 GiB4,337,668,8963.416mradermacher
I1-IQ3_M4.19 GiB4,494,619,4243.539mradermacher
Q3_K_M4.43 GiB4,761,784,8643.750mradermacher
I1-Q3_K_M4.43 GiB4,761,785,1203.750mradermacher
Q3_K_L4.78 GiB5,132,456,4804.042mradermacher
I1-Q3_K_L4.78 GiB5,132,456,7364.042mradermacher
I1-IQ4_XS4.83 GiB5,183,034,1444.081mradermacher
IQ4_XS4.86 GiB5,223,174,6884.113mradermacher
I1-IQ4_NL5.07 GiB5,443,146,5284.286mradermacher
I1-Q4_05.08 GiB5,459,202,8484.299mradermacher
Q4_K_S5.10 GiB5,478,928,9284.314mradermacher
I1-Q4_K_S5.10 GiB5,478,929,1844.314mradermacher
Q4_K_M5.37 GiB5,761,061,4084.537mradermacher
I1-Q4_K_M5.37 GiB5,761,061,6644.537mradermacher
I1-Q4_15.55 GiB5,963,371,2964.696mradermacher
Q5_K_S6.04 GiB6,483,595,8085.106mradermacher
I1-Q5_K_S6.04 GiB6,483,596,0645.106mradermacher
Q5_K_M6.19 GiB6,647,370,2725.235mradermacher
I1-Q5_K_M6.19 GiB6,647,370,5285.235mradermacher
Q6_K7.07 GiB7,589,073,4405.976mradermacher
I1-Q6_K7.07 GiB7,589,073,6965.976mradermacher
Q8_09.15 GiB9,827,152,4167.739mradermacher
F1617.22 GiB18,490,683,93614.561mradermacher

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 5.32 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 Smilodon-9B-v1 need?
Q4_K_M is exactly 5,761,061,408 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 Smilodon-9B-v1'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 Smilodon-9B-v1 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.