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Olmo-3.1-32B-Instruct

allenai/Olmo-3.1-32B-Instruct

Olmo-3.1-32B-Instruct at Q4_K_M is exactly 19,482,034,976 bytes (18.14 GiB / 19.48 GB) — an effective 4.835 bits per weight, not the nominal 4. Its KV cache at 32K is 2.84 GiB, not the 8.00 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
32.2B
Architecture
olmo2
64 layers
Context
65,536
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
UD-IQ1_S6.75 GiB7,245,910,3361.798unsloth
UD-IQ1_M7.33 GiB7,865,225,5361.952unsloth
IQ2_XXS8.15 GiB8,756,307,4562.173bartowski
UD-IQ2_XXS8.30 GiB8,914,620,7362.212unsloth
IQ2_XS9.02 GiB9,685,607,9362.404bartowski
IQ2_S9.40 GiB10,088,697,7922.504bartowski
IQ2_M10.21 GiB10,965,569,4722.721bartowski
UD-IQ2_M10.29 GiB11,047,571,7762.742unsloth
Q2_K11.18 GiB12,005,941,6322.980bartowski
Q2_K11.18 GiB12,005,942,0162.980unsloth
Q2_K_L11.29 GiB12,126,275,6163.010unsloth
Q2_K_L11.65 GiB12,507,331,6163.104bartowski
IQ3_XXS11.68 GiB12,540,399,5523.112bartowski
UD-IQ3_XXS11.78 GiB12,650,377,5363.140unsloth
IQ3_XS12.45 GiB13,371,427,6483.319bartowski
Q3_K_S13.09 GiB14,058,244,9283.489bartowski
Q3_K_S13.09 GiB14,058,245,3123.489unsloth
IQ3_M13.48 GiB14,476,036,9283.593bartowski
Q3_K_M14.53 GiB15,600,962,3683.872bartowski
Q3_K_M14.53 GiB15,600,962,7523.872unsloth
Q3_K_L15.75 GiB16,912,993,0884.198bartowski
IQ4_XS16.14 GiB17,332,139,2324.302bartowski
IQ4_XS16.16 GiB17,348,184,0964.306unsloth
IQ4_NL17.06 GiB18,312,873,6324.545bartowski
IQ4_NL17.06 GiB18,312,874,0164.545unsloth
Q4_017.08 GiB18,341,709,4724.552bartowski
Q4_017.08 GiB18,341,709,8564.552unsloth
Q4_K_S17.15 GiB18,415,109,7924.570bartowski
Q4_K_S17.15 GiB18,415,110,1764.570unsloth
Q4_K_M18.14 GiB19,482,034,9764.835lmstudio-community
Q4_K_M18.14 GiB19,482,035,8724.835bartowski
Q4_K_M18.14 GiB19,482,036,2564.835unsloth
Q4_K_L18.50 GiB19,863,092,2564.930bartowski
Q4_118.86 GiB20,253,370,9125.027bartowski
Q4_118.86 GiB20,253,371,2965.027unsloth
Q5_K_S20.71 GiB22,235,811,2325.519bartowski
Q5_K_S20.71 GiB22,235,811,6165.519unsloth
Q5_K_M21.29 GiB22,859,713,9525.673bartowski
Q5_K_M21.29 GiB22,859,714,3365.673unsloth
Q5_K_L21.58 GiB23,176,592,4165.752bartowski

KV cache by context

computed per layer — this model uses sliding-window attention
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0961.00 GiB1.00 GiB16 / 48 / 0
8,1921.34 GiB2.00 GiB1.49×16 / 48 / 0
16,3841.84 GiB4.00 GiB2.17×16 / 48 / 0
32,7682.84 GiB8.00 GiB2.81×16 / 48 / 0
65,5364.84 GiB16.00 GiB3.30×16 / 48 / 0
131,0728.84 GiB32.00 GiB3.62×16 / 48 / 0

48 of 64 layers cache only a 4,096-token window rather than the full context, on a period of . 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 16.89 GiB. The real file is 18.14 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 8.00 GiB at 32K context where the real figure is 2.84 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json
Layers
64
Attention heads
40
KV heads
8
Head dim
128
Hidden size
5120
Vocab
100,278
Sliding window
4096
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Olmo-3.1-32B-Instruct need?
Q4_K_M is exactly 19,482,034,976 bytes (18.14 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Olmo-3.1-32B-Instruct's KV cache?
2.84 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 Olmo-3.1-32B-Instruct 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.