DeepSeek V4 Performance Analysis: 1.6T Day 0 to Day 43 Scaling Trends

SemiAnalysis's longitudinal performance study of DeepSeek V4 across multiple hardware backends provides crucial empirical data for practitioners considering large model deployment strategies. Tracking a 1.6 trillion parameter model from initial rollout through optimization iterations reveals how inference performance matures as software stacks mature and models are fine-tuned for specific hardware characteristics.

The multi-platform analysis—spanning Huawei accelerators, AMD MI355X, and NVIDIA's latest hardware—demonstrates that truly local deployment increasingly means hardware heterogeneity. This data is invaluable for understanding the practical performance tradeoffs when deploying models like DeepSeek V4 locally versus cloud alternatives. The 43-day performance trajectory suggests that inference optimization is an ongoing process, not a static achievement.

For teams running local LLMs at scale, these benchmarks highlight the importance of continuous profiling and optimization. The patterns may indicate which architectural choices (quantization strategies, batch sizes, memory management) yield the best returns on different hardware—essential information for achieving production-grade local inference.

Source: Google News · Relevance: 8/10