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OLMo 3 7B Instruct

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: $0.20 · Output: $0.20

Hardware & System Requirements

Precision

Native

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

>= 4.57.1

ArchitecturePrecisionAttention kernel

Blackwell, Hopper

Native FP16

FlashAttention-3

Ada Lovelace, Ampere

Native FP16

FlashAttention-2

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 4.1k · Context: 66K · Vocab: 100.3kx 32 layersRMSNormPre-AttentionMulti-Head Attention32Q / 32KV heads · SW: 4.1kHead dim: 128+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 11k+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#235

BenchmarkScoreRank

General Text

Text Arena

1074

187

Intelligence Index

Artificial Analysis

0.05

313

Rankings

Overall Rank

#235

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.

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.


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