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EuroLLM-22B-Instruct-2512

utter-project/EuroLLM-22B-Instruct-2512

EuroLLM-22B-Instruct-2512 at Q4_K_M is exactly 13,658,576,160 bytes (12.72 GiB / 13.66 GB) — an effective 4.827 bits per weight, not the nominal 4. Its KV cache at 32K is 6.75 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
22.6B
Architecture
llama
54 layers
Context
32,768
native (config.json)
License
apache-2.0

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
IQ2_XS6.53 GiB7,009,735,9682.477—bartowski
IQ2_S6.93 GiB7,437,604,1282.628—bartowski
IQ2_M7.45 GiB8,002,655,5202.828—bartowski
Q2_K8.06 GiB8,659,227,9363.060—bartowski
Q2_K8.06 GiB8,659,228,0963.060—mradermacher
IQ3_XXS8.34 GiB8,955,811,1043.165—bartowski
Q2_K_L8.78 GiB9,427,227,9363.332—bartowski
IQ3_XS8.93 GiB9,586,382,1123.388—bartowski
Q3_K_S9.35 GiB10,039,661,8563.548—bartowski
Q3_K_S9.35 GiB10,039,662,0163.548—mradermacher
IQ3_M9.72 GiB10,435,728,6723.688—bartowski
Q3_K_M10.35 GiB11,116,778,7843.929—bartowski
Q3_K_M10.35 GiB11,116,778,9443.929—mradermacher
Q3_K_L11.23 GiB12,054,205,7284.260—bartowski
Q3_K_L11.23 GiB12,054,205,8884.260—mradermacher
IQ4_XS11.47 GiB12,311,909,6644.351—bartowski
IQ4_XS11.55 GiB12,406,281,6644.384—mradermacher
Q4_012.09 GiB12,979,393,8244.587—bartowski
IQ4_NL12.09 GiB12,984,112,4164.589—bartowski
Q4_K_S12.13 GiB13,020,288,2884.601—bartowski
Q4_K_S12.13 GiB13,020,288,4484.601—mradermacher
Q4_K_M12.72 GiB13,658,576,1604.827—bartowski
Q4_K_M12.72 GiB13,658,576,3204.827—mradermacher
Q4_K_L13.26 GiB14,242,256,1605.033—bartowski
Q4_113.32 GiB14,307,284,2565.056—bartowski
Q5_K_S14.60 GiB15,672,923,4245.539—bartowski
Q5_K_S14.60 GiB15,672,923,5845.539—mradermacher
Q5_K_M14.94 GiB16,042,251,5525.669—bartowski
Q5_K_M14.94 GiB16,042,251,7125.669—mradermacher
Q5_K_L15.39 GiB16,527,627,5525.841—bartowski
Q6_K17.30 GiB18,574,906,6566.564—bartowski
Q6_K17.30 GiB18,574,906,8166.564—mradermacher
Q6_K_L17.65 GiB18,955,834,6566.699—bartowski
Q8_022.41 GiB24,057,222,4328.502—bartowski
Q8_022.41 GiB24,057,222,5928.502—mradermacher
BF1642.17 GiB45,279,089,63216.002—bartowski

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0960.84 GiB0.84 GiB—54 / 0 / 0
8,1921.69 GiB1.69 GiB—54 / 0 / 0
16,3843.38 GiB3.38 GiB—54 / 0 / 0
32,7686.75 GiB6.75 GiB—54 / 0 / 0
65,53613.50 GiB13.50 GiB—54 / 0 / 0
131,07227.00 GiB27.00 GiB—54 / 0 / 0

Compare with

same modality, comparable size
vs Qwen3-Coder-30B-A3B-Instructvs Qwen3.6-27Bvs Qwen3.8-27Bvs gemma-4-12B-it-qat-q4_0-unquantizedvs gemma-4-E4B-itvs Qwen3-30B-A3B-Thinking-2507All alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 11.86 GiB. The real file is 12.72 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
54
Attention heads
48
KV heads
8
Head dim
128
Hidden size
6144
Vocab
128,000
Sliding window
none
SWA period
—
MLA
no
Experts
—
Experts per token
—
use_sliding_window
—

Questions people ask

How much VRAM does EuroLLM-22B-Instruct-2512 need?
Q4_K_M is exactly 13,658,576,160 bytes (12.72 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is EuroLLM-22B-Instruct-2512's KV cache?
6.75 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 EuroLLM-22B-Instruct-2512 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.