GPT-OSS 120B
Active Parameters
117B
Context Length
128K
Modality
Text
Architecture
Mixture of Experts (MoE)
License
Apache 2.0
Release Date
5 Aug 2025
Knowledge Cutoff
Jun 2024
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
13x RTX 4090
24GB VRAM
Datacenter
4x NVIDIA A100
80GB VRAM
Apple Silicon
3x Apple M3 Max
128GB VRAM
128,000 tokens
Consumer
13x RTX 4090
24GB VRAM
Datacenter
4x NVIDIA A100
80GB VRAM
Apple Silicon
3x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#51
| Benchmark | Score | Rank |
|---|---|---|
Summarization | 0.976 | 🥉 3 |
General Knowledge | 0.9 | 🥉 3 |
Graduate-Level QA | 0.801 | 26 |
Coding | 0.42 | 28 |
Professional Knowledge | 0.79 | 41 |
General Text | 1352 | 116 |
Rankings
Overall Rank
#51
Coding Rank
#71
About GPT-OSS 120B
GPT-OSS 120B is a large open-weight model from OpenAI, designed to operate in data centers and on high-end desktops and laptops. It is developed to support advanced reasoning, agentic tasks, and diverse developer use cases, functioning as a text-only model for both input and output modalities.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
64
Key-Value Heads
8
Attention Head Dimension
64
Position Embedding
Absolute Position Embedding
RoPE Theta
150,000
Sliding Window Attention
Yes
Sliding Window Size
128
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
2,880
Number of Layers
36
FFN Intermediate Size (Dense)
2,880
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
201,088
Mixture of Experts
Total Expert Parameters
5.1B
Number of Experts
128
Active Experts
4
Shared Experts
-
FFN Intermediate Size (per Expert)
-
Dense Layers Before MoE
-