APEX-1: Exploration of a Unified Architecture Fusing the Essence of Nine Top Models

APEX-1 is an ambitious open-source large language model architecture project that aims to integrate the innovative designs of nine mainstream models including Claude, GPT-4.5, DeepSeek-V3, Qwen3, Gemma4, etc., to build a training-ready next-generation AI infrastructure. The project attempts to address the problem of scattered advantages in the current large model field and become an 'all-encompassing' model solution through systematic integration.

The large language model field showed a prosperous scene from 2024 to 2025, with models from institutions like OpenAI, Anthropic, Meta, and Alibaba excelling in architecture, training, inference, etc. However, advantages are scattered across independent projects, making it difficult for developers to enjoy all innovations within a unified framework. APEX-1 was proposed against this background, dedicated to integrating the advantages of various parties.

APEX-1 draws inspiration from nine models:

1. Claude: Security and long-context processing, Constitutional AI and RLHF alignment methods;
2. GPT-4.5: Reasoning ability, multimodal processing, MoE architecture expansion and computational optimization;
3. DeepSeek-V3: High cost-effectiveness, MLA mechanism, FP8 training, load-balanced MoE;
4. Qwen3: Chinese understanding and multilingual capabilities, model compression and deployment efficiency;
5. Gemma4: Edge-side optimization, quantization and inference acceleration;
6. GLM-4: Autoregressive fill-in architecture, balanced understanding and generation capabilities;
7. KIMI: Ultra-long context window (millions of tokens);
8. MiniMax: Multimodal and voice interaction;
9. Llama3: Concise and efficient architecture, open-source ecosystem and community foundation.

Architecture integration faces three major challenges:

• Architectural style compatibility: Pure decoder vs encoder-decoder, dense vs sparse MoE, different positional encodings;
• Unified training strategy: Pre-training data ratio, post-training alignment methods (SFT/RLHF/DPO, etc.), multi-stage training;
• Balance of inference optimization: Different needs of cloud, edge, and real-time interaction scenarios.

Possible design directions:

• Modular Transformer: Replaceable attention (MHA/MLA/GQA, etc.), configurable FFN, flexible positional encoding;
• Phased training framework: Large-scale pre-training → continuous pre-training → SFT → alignment training;
• Multimodal extension interfaces: Visual encoder integration, audio processing, tool usage interfaces.

APEX-1 emphasizes 'training readiness' and provides complete infrastructure:

• Data pipeline: Preprocessing (cleaning/deduplication/quality filtering), dynamic data mixing and curriculum learning;
• Training framework: Distributed parallelism (data/model/pipeline), mixed-precision training, fault tolerance and recovery;
• Evaluation and alignment tools: Automatic evaluation benchmarks (MMLU/HumanEval, etc.), preference data generation, automated red team testing.

GPU Resource Requirements: Training a 70B model requires a large amount of video memory (model + optimizer + gradients + activations), with a computational volume of approximately 4.2e18 FLOPs (70B × 1 trillion tokens), requiring thousands to tens of thousands of GPU hours.

Acquisition Channels: Cloud computing platforms, academic clusters, corporate sponsorships, decentralized computing.

Community Participation: Contributor roles include architecture design, engineering implementation, data work, evaluation testing, documentation and tutorials; open-source strategies need to consider licenses (Apache/MIT/GPL, etc.), weight release, and community governance.

Potential Advantages: Comprehensive design avoids the limitations of a single model, community-driven rapid iteration, training readiness reduces the threshold for reproduction.

Challenges Faced: High engineering complexity, large resource requirements, competitive pressure from commercial models, risk of technical debt.

Conclusion: APEX-1 is an idealistic attempt. Its success depends on community input and resource support, and its exploration has far-reaching significance for the innovation boundaries of the AI field and the positioning of open-source communities.