DeepSeek-V3 is a sparse Transformer: it has 671 billion total parameters, but the DeepSeek-AI technical report says 37 billion are activated for each token. Its design combines Multi-head Latent Attention (MLA) with DeepSeekMoE expert layers, then adds an auxiliary-loss-free load-balancing strategy and multi-token prediction (MTP). Sparsity reduces the parameters used in a token’s computation; it does not make the full model checkpoint small or remove the practical demands of deployment.
How DeepSeek-V3’s architecture fits together
DeepSeek-V3 remains within the standard Transformer framework. Its attention layers use MLA, while its feed-forward layers use DeepSeekMoE, a mixture-of-experts (MoE) design. The DeepSeek-V3 technical report describes MLA and DeepSeekMoE as designs carried forward from DeepSeek-V2; auxiliary-loss-free load balancing and MTP are among the additions in V3. It is therefore an evolution of an existing Transformer design, not a different neural-network family. DeepSeek-V3 technical report
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What 671B total and 37B active parameters mean
The report gives DeepSeek-V3 671 billion total parameters and 37 billion activated parameters per token. Total parameters count the model’s weights across its experts and other components. Activated parameters count the subset involved in processing a given token. In an MoE layer, a routing mechanism directs each token to selected experts rather than running every expert for every token. That sparsity can reduce per-token computation compared with using all the weights densely, but it does not mean the full set of weights can be discarded at inference.
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How DeepSeekMoE organizes its experts
DeepSeekMoE is designed to let different experts handle different aspects of computation while keeping the number used for each token limited. Its foundational design paper describes two mechanisms: splitting experts into finer-grained units and activating more of those units, and keeping some experts shared so they can capture common knowledge instead of duplicating it across routed experts. The authors present these choices as ways to encourage specialization and reduce redundancy; they do not establish that each expert has a simple, human-readable specialty. DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
What MLA changes about attention memory
In ordinary attention, a model typically keeps key and value representations in a key-value (KV) cache during autoregressive inference so it can reuse prior context as it generates more tokens. MLA compresses keys and values jointly into a low-rank latent representation and reconstructs what is needed for attention. DeepSeek-AI presents this as a way to reduce KV-cache size. MLA does not eliminate the cache, and by itself it does not make serving the full model inexpensive. DeepSeek-V3 technical report
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How V3 balances expert traffic
An MoE router has to distribute tokens among experts. If traffic becomes uneven, some experts may be overloaded while others are underused. DeepSeek-AI says V3 uses an auxiliary-loss-free strategy to encourage balance while reducing the performance degradation associated with balancing objectives. This is the authors’ design claim, not a claim that routing overhead or operational trade-offs disappear. DeepSeek-V3 technical report
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Rather than training only to predict the next token, MTP extends the objective so the model predicts multiple future tokens at each position. The report says this can make training signals denser and help prepare representations for future prediction. It also describes using MTP in speculative decoding, where proposed future tokens can be checked as part of generation. The repository includes a 14B MTP module in the downloadable model files. MTP’s presence does not guarantee a particular inference speedup: results depend on the model-serving implementation and workload. DeepSeek-V3 technical report DeepSeek-V3 official repository README
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What the published training figures do—and do not—show
The DeepSeek-V3 technical report states that the model was pretrained on 14.8 trillion tokens and that full training used 2.788 million H800 GPU hours. These are figures reported by the model developers, not independently audited measurements. GPU-hour totals also should not be compared directly with another training run unless the accounting methods and scope match. DeepSeek-V3 technical report
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the architecture implies for deployment
Sparse activation can lower computation per token relative to running every parameter densely, but deployment still has to accommodate the model’s weights, KV cache, MTP component where used, and serving workload. The technical report says the recommended deployment unit is relatively large and might burden small teams. It does not establish one minimum GPU count or memory requirement that applies across quantization choices, inference engines, context lengths, and throughput targets. For an actual deployment, consult the current official repository and deployment documentation and size the setup for the intended configuration. DeepSeek-V3 technical report DeepSeek-V3 official repository README
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