OLMo 3 32B Base
Parameters
32B
Context Length
66K
Modality
Text
Architecture
Dense
KV Cache Rate
256 KB/tok
License
Apache 2.0
Release Date
25 Nov 2025
Knowledge Cutoff
Dec 2024
API Pricing (per 1M)
Input: $0.15 · Output: $0.50
Hardware & System Requirements
Precision
Context
Workload Memory: 19.40 GB
INT4 · 8K context
Base Weights: 14.90 GB
KV Cache: 2.00 GB
Runtime & Graphs: 2.50 GB
Minimum Required Hardware
1x NVIDIA L4 24GB
19.4 / 21.6 GB usable
2x RTX 4090 24GB
19.4 / 43.2 GB usable · TP 2
Apple M3 Pro 36GB
19.4 / 27.0 GB usable
Determine requirements for your specific workload:
Open in VRAM Calculator
Architecture Diagram
Evaluation Benchmarks
Rank
#207
| Benchmark | Score | Rank |
|---|---|---|
Intelligence Index | auto 0.07 | 288 |
Rankings
Overall Rank
#207
Coding Rank
-
About OLMo 3 32B Base
OLMo 3 32B Base is a 32B parameter open foundation model from Ai2 engineered for transparent NLP research, mathematical reasoning, and coding. It features hybrid sliding-window attention and YaRN RoPE scaling across a 65K context window.
Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
40
Key-Value Heads
8
Position Embedding
Absolute Position Embedding
RoPE Theta
500,000
Sliding Window Attention
Yes
Sliding Window Size
4,096
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Base Precision
BF16
Hidden Dimension Size
5,120
Number of Layers
64
FFN Intermediate Size (Dense)
27,648
Tokenizer
Vocabulary Size
100,278
About OLMo 3
OLMo (Open Language Model) is a series of fully open language models designed to enable the science of language models. Released by the Allen Institute for AI (Ai2), OLMo 3 provides complete access to training data (Dolma 3), code, checkpoints, logs, and evaluation methodologies. The family includes Base models for pretraining research, Instruct variants for chat and tool use, and Think variants with chain-of-thought reasoning capabilities. All models are trained with staged approach including pretraining, mid-training, and long-context phases.