EPFLiGHT · text

Gemma-3-27B-MeditronFO

EPFLiGHT/Gemma-3-27B-MeditronFO

Gemma-3-27B-MeditronFO at Q4_K_M is exactly 17,339,606,784 bytes (16.15 GiB / 17.34 GB) — an effective 4.809 bits per weight, not the nominal 4. Its KV cache at 32K is 3.11 GiB, not the 15.50 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
28.8B
Architecture
gemma3
62 layers
Context
native (config.json)
License
gemma

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S6.26 GiB6,726,783,6481.866mradermacher
I1-IQ1_M6.76 GiB7,259,781,7922.014mradermacher
I1-IQ2_XXS7.59 GiB8,148,112,0322.260mradermacher
I1-IQ2_XS8.29 GiB8,901,440,1602.469mradermacher
I1-IQ2_S8.74 GiB9,388,110,6242.604mradermacher
I1-IQ2_M9.41 GiB10,098,774,8162.801mradermacher
I1-Q2_K_S9.52 GiB10,219,982,1122.835mradermacher
Q2_K10.21 GiB10,966,256,6403.042mradermacher
I1-Q2_K10.21 GiB10,966,256,9283.042mradermacher
I1-IQ3_XXS10.54 GiB11,322,180,3843.140mradermacher
I1-IQ3_XS11.33 GiB12,167,935,3923.375mradermacher
Q3_K_S11.90 GiB12,773,315,7123.543mradermacher
I1-IQ3_S11.90 GiB12,773,316,0003.543mradermacher
I1-Q3_K_S11.90 GiB12,773,316,0003.543mradermacher
I1-IQ3_M12.25 GiB13,152,775,5843.648mradermacher
Q3_K_M13.08 GiB14,043,341,9523.895mradermacher
I1-Q3_K_M13.08 GiB14,043,342,2403.895mradermacher
Q3_K_L14.11 GiB15,149,163,6484.202mradermacher
I1-Q3_K_L14.11 GiB15,149,163,9364.202mradermacher
I1-IQ4_XS14.45 GiB15,516,315,1684.304mradermacher
IQ4_XS14.57 GiB15,642,758,4004.339mradermacher
I1-Q4_015.28 GiB16,410,892,3204.552mradermacher
Q4_K_S15.34 GiB16,466,974,4644.567mradermacher
I1-Q4_K_S15.34 GiB16,466,974,7524.567mradermacher
Q4_K_M16.15 GiB17,339,606,7844.809mradermacher
I1-Q4_K_M16.15 GiB17,339,607,0724.809mradermacher
I1-Q4_116.81 GiB18,048,314,4005.006mradermacher
Q5_K_S18.38 GiB19,736,313,6005.474mradermacher
I1-Q5_K_S18.38 GiB19,736,313,8885.474mradermacher
Q5_K_M18.85 GiB20,240,797,4405.614mradermacher
I1-Q5_K_M18.85 GiB20,240,797,7285.614mradermacher
Q6_K21.72 GiB23,323,312,5126.469mradermacher
I1-Q6_K21.72 GiB23,323,312,8006.469mradermacher
Q8_028.13 GiB30,205,705,3448.378mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.92 GiB1.94 GiB2.10×10 / 52 / 0
8,1921.23 GiB3.88 GiB3.14×10 / 52 / 0
16,3841.86 GiB7.75 GiB4.17×10 / 52 / 0
32,7683.11 GiB15.50 GiB4.98×10 / 52 / 0
65,5365.61 GiB31.00 GiB5.53×10 / 52 / 0
131,07210.61 GiB62.00 GiB5.84×10 / 52 / 0

52 of 62 layers cache only a 1,024-token window rather than the full context, on a period of 6. 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 15.11 GiB. The real file is 16.15 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 15.50 GiB at 32K context where the real figure is 3.11 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
62
Attention heads
32
KV heads
16
Head dim
128
Hidden size
5376
Vocab
Sliding window
1024
SWA period
6
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Gemma-3-27B-MeditronFO need?
Q4_K_M is exactly 17,339,606,784 bytes (16.15 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Gemma-3-27B-MeditronFO's KV cache?
3.11 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 Gemma-3-27B-MeditronFO 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.