OLMo 3 7B Instruct
Parameters
7B
Context Length
66K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
25 Oct 2025
Knowledge Cutoff
Dec 2024
API Pricing (per 1M)
Input: $0.10 · Output: $0.20
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
65,536 tokens
Consumer
3x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#193
| Benchmark | Score | Rank |
|---|---|---|
General Text | 1073 | 171 |
Intelligence Index | 0.05 | 226 |
Rankings
Overall Rank
#193
Coding Rank
-
About OLMo 3 7B Instruct
OLMo 3 7B Instruct is an instruction-tuned open-source model by Ai2 optimized for low-latency dialogue, multi-turn interactions, and local assistants. It combines verifiable post-training with full training pipeline transparency over a 65K context.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
32
Attention Head Dimension
-
Position Embedding
Absolute Position Embedding
RoPE Theta
500,000
Sliding Window Attention
Yes
Sliding Window Size
4,096
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
11,008
Multi-Token Prediction Heads
-
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.