MoE Architecture Comparison: Qwen3 30B-A3B vs. GPT-OSS 20B

This article provides a technical comparison between two recently released Mixture-of-Experts (MoE) transformer models: Alibaba’s Qwen3 30B-A3B (released April 2025) and OpenAI’s GPT-OSS 20B (released August 2025). Both models represent distinct approaches to MoE architecture design, balancing computational efficiency with performance across different deployment scenarios.

Model Overview

Feature	Qwen3 30B-A3B	GPT-OSS 20B
Total Parameters	30.5B	21B
Active Parameters	3.3B	3.6B
Number of Layers	48	24
MoE Experts	128 (8 active)	32 (4 active)
Attention Architecture	Grouped Query Attention	Grouped Multi-Query Attention
Query/Key-Value Heads	32Q / 4KV	64Q / 8KV
Context Window	32,768 (ext. 262,144)	128,000
Vocabulary Size	151,936	o200k_harmony (~200k)
Quantization	Standard precision	Native MXFP4
Release Date	April 2025	August 2025

Sources: Qwen3 Official Documentation, OpenAI GPT-OSS Documentation

Qwen3 30B-A3B Technical Specifications

Architecture Details

Qwen3 30B-A3B employs a deep transformer architecture with 48 layers, each containing a Mixture-of-Experts configuration with 128 experts per layer. The model activates 8 experts per token during inference, achieving a balance between specialization and computational efficiency.

Attention Mechanism

The model utilizes Grouped Query Attention (GQA) with 32 query heads and 4 key-value heads³. This design optimizes memory usage while maintaining attention quality, particularly beneficial for long-context processing.

Context and Multilingual Support

Native context length: 32,768 tokens
Extended context: Up to 262,144 tokens (latest variants)
Multilingual support: 119 languages and dialects
Vocabulary: 151,936 tokens using BPE tokenization

Unique Features

Qwen3 incorporates a hybrid reasoning system supporting both “thinking” and “non-thinking” modes, allowing users to control computational overhead based on task complexity.

GPT-OSS 20B Technical Specifications

Architecture Details

GPT-OSS 20B features a 24-layer transformer with 32 MoE experts per layer⁸. The model activates 4 experts per token, emphasizing wider expert capacity over fine-grained specialization.

Attention Mechanism

The model implements Grouped Multi-Query Attention with 64 query heads and 8 key-value heads arranged in groups of 8¹⁰. This configuration supports efficient inference while maintaining attention quality across the wider architecture.

Context and Optimization

Native context length: 128,000 tokens
Quantization: Native MXFP4 (4.25-bit precision) for MoE weights
Memory efficiency: Runs on 16GB memory with quantization
Tokenizer: o200k_harmony (superset of GPT-4o tokenizer)

Performance Characteristics

GPT-OSS 20B uses alternating dense and locally banded sparse attention patterns similar to GPT-3, with Rotary Positional Embedding (RoPE) for positional encoding¹⁵.

Architectural Philosophy Comparison

Depth vs. Width Strategy

Qwen3 30B-A3B emphasizes depth and expert diversity:

48 layers enable multi-stage reasoning and hierarchical abstraction
128 experts per layer provide fine-grained specialization
Suitable for complex reasoning tasks requiring deep processing

GPT-OSS 20B prioritizes width and computational density:

24 layers with larger experts maximize per-layer representational capacity
Fewer but more powerful experts (32 vs 128) increase individual expert capability
Optimized for efficient single-pass inference

MoE Routing Strategies

Qwen3: Routes tokens through 8 of 128 experts, encouraging diverse, context-sensitive processing paths and modular decision-making.

GPT-OSS: Routes tokens through 4 of 32 experts, maximizing per-expert computational power and delivering concentrated processing per inference step.

Memory and Deployment Considerations

Qwen3 30B-A3B

Memory requirements: Variable based on precision and context length
Deployment: Optimized for cloud and edge deployment with flexible context extension
Quantization: Supports various quantization schemes post-training

GPT-OSS 20B

Memory requirements: 16GB with native MXFP4 quantization, ~48GB in bfloat16
Deployment: Designed for consumer hardware compatibility
Quantization: Native MXFP4 training enables efficient inference without quality degradation

Performance Characteristics

Qwen3 30B-A3B

Excels in mathematical reasoning, coding, and complex logical tasks
Strong performance in multilingual scenarios across 119 languages
Thinking mode provides enhanced reasoning capabilities for complex problems

GPT-OSS 20B

Achieves performance comparable to OpenAI o3-mini on standard benchmarks
Optimized for tool use, web browsing, and function calling
Strong chain-of-thought reasoning with adjustable reasoning effort levels

Use Case Recommendations

Choose Qwen3 30B-A3B for:

Complex reasoning tasks requiring multi-stage processing
Multilingual applications across diverse languages
Scenarios requiring flexible context length extension
Applications where thinking/reasoning transparency is valued

Choose GPT-OSS 20B for:

Resource-constrained deployments requiring efficiency
Tool-calling and agentic applications
Rapid inference with consistent performance
Edge deployment scenarios with limited memory

Conclusion

Qwen3 30B-A3B and GPT-OSS 20B represent complementary approaches to MoE architecture design. Qwen3 emphasizes depth, expert diversity, and multilingual capability, making it suitable for complex reasoning applications. GPT-OSS 20B prioritizes efficiency, tool integration, and deployment flexibility, positioning it for practical production environments with resource constraints.

Both models demonstrate the evolution of MoE architectures beyond simple parameter scaling, incorporating sophisticated design choices that align architectural decisions with intended use cases and deployment scenarios.

Note: This article is inspired from the reddit post and diagram shared by Sebastian Raschka.

Sources

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