FitMyLLM
Allen AI/Dense

Allen AIOLMo 3.1 32B Think

We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding.

chatreasoningmathcoding
32B
Parameters
64K
Context length
11
Benchmarks
14
Quantizations
0
Architecture
Dense
Released
2026-01-20
Layers
64
KV Heads
8
Head Dim
128
Family
olmo

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
15.0 GB
13.5 + 1.5 KV
low
IQ3_XS3.5
16.0 GB
14.5 + 1.5 KV
low
Q3_K_S3.64
16.5 GB
15.0 + 1.5 KV
low
IQ3_M3.76
17.0 GB
15.5 + 1.5 KV
low
Q3_K_M4
18.0 GB
16.5 + 1.5 KV
low
Q3_K_L4.3
19.2 GB
17.7 + 1.5 KV
moderate
IQ4_XS4.46
19.8 GB
18.3 + 1.5 KV
moderate
Q4_K_S4.67
20.7 GB
19.2 + 1.5 KV
moderate
Q4_K_M4.89
21.5 GB
20.0 + 1.5 KV
good
Q5_K_S5.57
24.3 GB
22.8 + 1.5 KV
good
Q5_K_M5.7
24.8 GB
23.3 + 1.5 KV
good
Q6_K6.56
28.2 GB
26.7 + 1.5 KV
excellent
Q8_08.5
36.0 GB
34.5 + 1.5 KV
lossless
FP1616
66.0 GB
64.5 + 1.5 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN OLMO 3.1 32B THINK NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (11)

AIME77.3
AA Math77.3
MATH-50077.3
MMLU-PRO76.3
LiveCodeBench69.5
IFBench66.0
GPQA Diamond59.1
SciCode29.3
AA Intelligence13.9
AA Coding9.8
HLE6.0

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run olmo-3.1:32b-think-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000
NVIDIA L40G
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for OLMo 3.1 32B Think

Build Hardware for OLMo 3.1 32B Think
▸ SPEC SHEET

OLMo 3.1 32B Think32B Dense.

▸ SPECIFICATIONS
PARAMETERS
32B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
64K tokens
CAPABILITIES
chat, reasoning, math, coding
RELEASE DATE
2026-01-20
PROVIDER
Allen AI
FAMILY
olmo
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2513.5 GB82%
IQ3_XS3.514.5 GB84%
Q3_K_S3.6415.0 GB85%
IQ3_M3.7615.5 GB86%
Q3_K_M416.5 GB88%
Q3_K_L4.317.7 GB90%
IQ4_XS4.4618.3 GB92%
Q4_K_S4.6719.2 GB93%
Q4_K_M4.8920.0 GB94%
Q5_K_S5.5722.8 GB96%
Q5_K_M5.723.3 GB96%
Q6_K6.5626.7 GB97%
Q8_08.534.5 GB100%
FP161664.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO76.3
GPQA Diamond59.1
LiveCodeBench69.5
AIME77.3
HLE6.0
AA Intelligence13.9
AA Coding9.8
AA Math77.3
aa_ifbench66.0
aa_scicode29.3
MATH-50077.3
§ 02RUN COMMAND

Run OLMo 3.1 32B Think locally with Ollama — needs 20.0 GB VRAM at Q4_K_M:

$ollama run olmo-3.1:32b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M