Analyzing DeepSeek-V3 Model Performance

DeepSeek-R1/V3 is presented as a large transformer-based language model whose inference efficiency depends on its Multi-head Latent Attention (MLA), Mixture of Experts (MoE) layers, parallelized embeddings, dense decoder layers, and final RMS normalization. MLA processes full token sequences during prefill and incrementally reuses cached key-value and positional information during decoding to reduce latency, while MoE uses a gating network to activate eight of 256 routed experts per token, alongside a shared expert, to expand model capacity without executing every expert. The analysis uses representative settings including a hidden dimension of 7,168, an intermediate MLP dimension of 18,432, a maximum batch size of eight, and sequences up to 16,384 tokens. It applies roofline analysis to characterize FLOPs, memory reads and writes, and computational intensity for core operations such as matrix multiplication, RoPE, normalization, softmax, MLA, MoE, and MLPs, distinguishing compute-bound from memory-bound workloads. It also outlines distributed inference approaches based on expert, tensor, and data parallelism, aiming to inform efficient large-scale deployment and execution.

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