OLMo 3 7B Think
Parameters
7B
Context Length
66K
Modality
Text
Architecture
Dense
KV Cache Rate
512 KB/tok
License
Apache 2.0
Release Date
25 Oct 2025
Knowledge Cutoff
Dec 2024
API Pricing (per 1M)
Input: $0.15 · Output: $0.50
Hardware & System Requirements
Precision
Context
Workload Memory: 9.76 GB
INT4 · 8K context
Base Weights: 3.26 GB
KV Cache: 4.00 GB
Runtime & Graphs: 2.50 GB
Minimum Required Hardware
1x NVIDIA L4 24GB
9.8 / 21.6 GB usable
1x RTX 3060 12GB
9.8 / 10.8 GB usable
Apple M4 24GB
9.8 / 18.0 GB usable
Determine requirements for your specific workload:
Open in VRAM Calculator
Runtime requirements
Checkpoint format
FP16
CUDA
>= 11.8
PyTorch
>= 2.1
Transformers
>= 4.57.0
| Architecture | Precision | Attention kernel |
|---|---|---|
Blackwell, Hopper | Native FP16 | FlashAttention-3 |
Ada Lovelace, Ampere | Native FP16 | FlashAttention-2 |
Architecture Diagram
Evaluation Benchmarks
Rank
#180
| Benchmark | Score | Rank |
|---|---|---|
General Text | 1306 | 156 |
Intelligence Index | auto 0.06 | 306 |
Rankings
Overall Rank
#180
Coding Rank
-
About OLMo 3 7B Think
OLMo 3 7B Think is an open reasoning model by Ai2 engineered to generate transparent intermediate thinking tokens before answering. Trained with RLVR on verifiable datasets, it supports interpretable problem-solving across a 65K context.
Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
32
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
FP16
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
11,008
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.