DeepSeek V3
A large-scale Mixture-of-Experts (MoE) language model released by DeepSeek, introducing innovations in MoE engineering challenges such as load balancing. The model employs mechanisms including auxiliary-loss-free load balancing to improve training efficiency.
Core Facts
According to official disclosure, DeepSeek's training cost was only approximately $6 million.
DeepSeek R1 does not support Function Calling by default, whereas DeepSeek V3 supports Function Calling.
DeepSeek introduced MTP technology as early as version V3.
Timeline (last 90 days)
On the prefill sizes of DeepSeek V2 and V3, FlyDSL Flex Attention achieved close to a 2x improvement.
The Mixture of Experts (MoE) architecture is the core design adopted by a new generation of large models such as DeepSeek V3, where a routing mechanism dynamically selects a small number of experts to activate each time an input is processed.
The decode unit of DeepSeek V3 uses 144-card EP, amortizing the weight load overhead across large batches and freeing up GPU memory to increase batch size.
Most of Sebastian's course code runs on a Mac mini; the goal of the course is to demonstrate the principles of reasoning techniques using small models, rather than to replicate ultra-large models such as DeepSeek V3/V4.
Gemini, Claude, and DeepSeek V3 all support Function Calling.
The article recommends integrating the official release of DeepSeek V3 into Claude Code, citing its excellent performance, fast speed, affordable pricing, and the absence of compliance risks since data flows domestically.
Qwen 2.5 and DeepSeek V3 are major releases involving entirely new pre-training processes and architectural improvements.
Techniques such as GQA and MLA can significantly compress KV cache size; DeepSeek V3 adopts MLA to reduce memory overhead in long-context scenarios.
DeepSeek V3 was the first to validate the feasibility of the MoE route.
The project's Star count surpassed the combined total accumulated over more than a year by two star models from the same organization (speculated by the industry to be DeepSeek V3 and R1).
All Facts (16)
According to official disclosure, DeepSeek's training cost was only approximately $6 million.
80%VerifiedDeepSeek R1 does not support Function Calling by default, whereas DeepSeek V3 supports Function Calling.
80%VerifiedDeepSeek introduced MTP technology as early as version V3.
75%VerifiedQwen3.5's parameter activation rate is 4.3%, while DeepSeek V3's 671B model has an activation rate of 5.5%.
65%UnverifiedDeepSeek经历了从V2到V3再到R1的模型迭代，模型能力和交互方式都在快速变化
95%UnverifiedDeepSeek V3版本参数规模达到671B MoE架构
90%UnverifiedDeepSeek V3在中文创意写作、角色扮演和长文本理解方面表现尤为突出
75%UnverifiedGemini, Claude, and DeepSeek V3 all support Function Calling.
60%UnverifiedOn the prefill sizes of DeepSeek V2 and V3, FlyDSL Flex Attention achieved close to a 2x improvement.
50%UnverifiedThe Mixture of Experts (MoE) architecture is the core design adopted by a new generation of large models such as DeepSeek V3, where a routing mechanism dynamically selects a small number of experts to activate each time an input is processed.
50%UnverifiedThe decode unit of DeepSeek V3 uses 144-card EP, amortizing the weight load overhead across large batches and freeing up GPU memory to increase batch size.
50%UnverifiedMost of Sebastian's course code runs on a Mac mini; the goal of the course is to demonstrate the principles of reasoning techniques using small models, rather than to replicate ultra-large models such as DeepSeek V3/V4.
50%UnverifiedThe article recommends integrating the official release of DeepSeek V3 into Claude Code, citing its excellent performance, fast speed, affordable pricing, and the absence of compliance risks since data flows domestically.
50%UnverifiedQwen 2.5 and DeepSeek V3 are major releases involving entirely new pre-training processes and architectural improvements.
50%UnverifiedTechniques such as GQA and MLA can significantly compress KV cache size; DeepSeek V3 adopts MLA to reduce memory overhead in long-context scenarios.
50%UnverifiedDeepSeek V3 was the first to validate the feasibility of the MoE route.
50%