这是豆包和GPT-4两种不同的approach的优劣比较，according to ChapGPT:
Doubao’s sparse Mixture of Experts (MoE) design and GPT-4’s dense model architecture represent two distinct approaches to scaling large language models. Here’s how they compare in terms of advantages and disadvantages:
Advantages of Doubao’s Sparse MoE Design
1. Computational Efficiency – Sparse MoE activates only a subset of the total model parameters for any given input. This allows for a model with a high total parameter count while keeping the computational cost per inference lower than a dense model of the same scale.
2. Scalability – MoE architectures scale more easily by adding more experts (specialized subnetworks), leading to better performance on diverse tasks without linearly increasing inference costs.
3. Specialization – Different experts can specialize in different domains, potentially improving task-specific performance and generalization compared to a monolithic dense model.
4. Energy and Cost Savings – Because only a few experts are active per token, power consumption and inference costs are reduced, making large-scale deployment more sustainable.
Disadvantages of Doubao’s Sparse MoE Design
1. Latency from Routing Overhead – Sparse MoE requires a gating mechanism to decide which experts should process each input, adding computational overhead and potential latency.
2. Training Complexity – Training an MoE model is more complex than training a dense model, requiring careful balancing of expert load (avoiding underuse or overuse of specific experts) and preventing mode collapse (where a few experts dominate).
3. Increased Memory Footprint – Even if only a subset of experts is active at a time, all experts exist in memory, which can lead to higher storage and memory bandwidth requirements compared to a dense model with the same active parameter count.
4. Inference Challenges – Sparse MoE models require more complex inference infrastructure, making them harder to deploy in environments that do not support dynamic expert routing efficiently.
Advantages of GPT-4’s Dense Model
1. Predictable and Optimized Inference – Since all parameters are always active, inference is more predictable and optimized for existing AI hardware architectures.
2. Simpler Training and Deployment – Dense models avoid the complexities of MoE routing, expert balancing, and specialized hardware needs, making them easier to train and deploy across different environments.
3. Better Parameter Utilization – All parameters contribute to every inference step, meaning there is no risk of underutilized experts, which can be a problem in MoE models.
4. More Robust to Edge Cases – Dense models tend to be more stable across a wide range of inputs since they do not rely on expert selection mechanisms that might misroute certain inputs.
Disadvantages of GPT-4’s Dense Model
1. Higher Computation Costs – A dense model of the same total parameter count as an MoE model would be far more computationally expensive to run, as all parameters are used at all times.
2. Limited Specialization – While dense models can generalize well, they do not inherently provide specialized pathways like MoE, which can limit performance on domain-specific tasks.
3. Scaling Limitations – As models grow larger, computational demands increase exponentially, making dense models more difficult to scale efficiently compared to sparse MoE architectures.
Conclusion
Doubao’s sparse MoE design is advantageous for efficiency, scalability, and specialization but introduces challenges in routing, training, and deployment. GPT-4’s dense model is more predictable and stable but suffers from high computational costs and limited specialization. The choice between them depends on the trade-offs between efficiency, scalability, and deployment complexity.
看来也不需要那么多的核电站和上千亿的投资也可以做高质量的LLM和AI。
你可以侮辱你自己，别把美国人那套loser心态带到紫檀来
以买去开发专用AI系统
Doubao’s sparse Mixture of Experts (MoE) design and GPT-4’s dense model architecture represent two distinct approaches to scaling large language models. Here’s how they compare in terms of advantages and disadvantages:
Advantages of Doubao’s Sparse MoE Design
1. Computational Efficiency – Sparse MoE activates only a subset of the total model parameters for any given input. This allows for a model with a high total parameter count while keeping the computational cost per inference lower than a dense model of the same scale.
2. Scalability – MoE architectures scale more easily by adding more experts (specialized subnetworks), leading to better performance on diverse tasks without linearly increasing inference costs.
3. Specialization – Different experts can specialize in different domains, potentially improving task-specific performance and generalization compared to a monolithic dense model.
4. Energy and Cost Savings – Because only a few experts are active per token, power consumption and inference costs are reduced, making large-scale deployment more sustainable.
Disadvantages of Doubao’s Sparse MoE Design
1. Latency from Routing Overhead – Sparse MoE requires a gating mechanism to decide which experts should process each input, adding computational overhead and potential latency.
2. Training Complexity – Training an MoE model is more complex than training a dense model, requiring careful balancing of expert load (avoiding underuse or overuse of specific experts) and preventing mode collapse (where a few experts dominate).
3. Increased Memory Footprint – Even if only a subset of experts is active at a time, all experts exist in memory, which can lead to higher storage and memory bandwidth requirements compared to a dense model with the same active parameter count.
4. Inference Challenges – Sparse MoE models require more complex inference infrastructure, making them harder to deploy in environments that do not support dynamic expert routing efficiently.
Advantages of GPT-4’s Dense Model
1. Predictable and Optimized Inference – Since all parameters are always active, inference is more predictable and optimized for existing AI hardware architectures.
2. Simpler Training and Deployment – Dense models avoid the complexities of MoE routing, expert balancing, and specialized hardware needs, making them easier to train and deploy across different environments.
3. Better Parameter Utilization – All parameters contribute to every inference step, meaning there is no risk of underutilized experts, which can be a problem in MoE models.
4. More Robust to Edge Cases – Dense models tend to be more stable across a wide range of inputs since they do not rely on expert selection mechanisms that might misroute certain inputs.
Disadvantages of GPT-4’s Dense Model
1. Higher Computation Costs – A dense model of the same total parameter count as an MoE model would be far more computationally expensive to run, as all parameters are used at all times.
2. Limited Specialization – While dense models can generalize well, they do not inherently provide specialized pathways like MoE, which can limit performance on domain-specific tasks.
3. Scaling Limitations – As models grow larger, computational demands increase exponentially, making dense models more difficult to scale efficiently compared to sparse MoE architectures.
Conclusion
Doubao’s sparse MoE design is advantageous for efficiency, scalability, and specialization but introduces challenges in routing, training, and deployment. GPT-4’s dense model is more predictable and stable but suffers from high computational costs and limited specialization. The choice between them depends on the trade-offs between efficiency, scalability, and deployment complexity.
其实DS开源了，但凡OAI的人有点踏实的精神，你逆向一下也可以猜猜人家的算法架构是怎样的。那个Altman就是一个搅屎棍，把人工智能的方向给带偏了，谷歌的Demis曾经批评过。其实生成式人工智能只是一个分支而已，真正打开AGI的大门还得从RL入手。
不是有人已经说了，我们要的人工智能不是写诗画画，而我却只能做饭洗碗。真正的人工智能是能帮我做饭帮我洗碗，而我去写诗和画画。
一只刚出生的鸟，从来没看过墙，却知道要飞跃它，你觉得那是大数据训练出来的？那可怜的小鸟得有多大的脑袋，还没收集完数据就撞墙撞死了
产生联想只是一（大）步之遥。
deepseek 完全真正open source，奥特曼肯定一行一行对过code。LOL
openAI被扇耳光子了。
把自己的母国rank第一有好感,,包括日本,韩国,台湾,印度等给母国极高的分。就是Chinese-American rank China给个pathetic 的41。
谁敢说是被偷了。 小看中国，迟早被打脸
其实我们人类没那么高级，情感上不接受不见得就是事实
一失足成千古恨。
我当时很生气，那些个反党反社会主义的言论我是一定要打回去的。。结果一桌同学就看着我把他说得是节节后退，最后他说有机会让我自己去资本主义社会看看，我的想法就会有些不同。。
三十年过后如果我还有机会见到我同学的爹，我会跟他说当年我年幼无知，又不了解日本，所以当时说错了很多话。。
他当年说的没错。。
想法就不一样了。。
就不入籍,比你这号人有傲骨诚实多了!