What is the inference cost of DeepSeek's models?

The inference cost of DeepSeek’s models depends on factors like model size, hardware efficiency, and deployment optimizations. Smaller models, such as the 7B parameter versions, are designed for lower computational requirements, making them cheaper to run compared to larger models like the 67B parameter variants. Costs scale with model complexity: larger models demand more GPU/TPU memory and compute time, increasing expenses. For example, running a 7B model on an A100 GPU might cost around $0.50 per million tokens, while a 67B model could exceed $3 per million tokens due to higher resource usage. Hardware choice also matters—using newer GPUs like H100s improves speed but raises hourly rates, while older hardware reduces upfront costs but may increase latency.

DeepSeek employs architectural optimizations to balance performance and cost. Their Mixture-of-Experts (MoE) models, such as DeepSeek-MoE, activate only a subset of neural network “experts” per inference, reducing computation. For instance, a 16B MoE model might use 2-4 experts per query, cutting GPU memory usage by 40% compared to a dense model of the same size. Quantization techniques like 4-bit or 8-bit precision further lower costs by shrinking model weights, enabling smaller models to run on consumer-grade GPUs. A 7B model quantized to 4-bit could operate on a single 24GB GPU instead of requiring multiple high-end cards, slashing infrastructure expenses. These optimizations make DeepSeek’s models accessible for applications like chatbots or document analysis without prohibitive costs.

Developers can reduce inference costs through framework optimizations and deployment strategies. DeepSeek provides tools like vLLM and TensorRT-LLM integrations, which improve throughput by 2-4x via techniques like continuous batching and kernel fusion. For example, processing 100 requests in parallel with vLLM might reduce latency from 500ms to 150ms per token on an A100. Caching mechanisms for frequent queries and using spot instances for batch processing (e.g., overnight data analysis) also help manage expenses. Additionally, DeepSeek’s API offers tiered pricing based on usage volume, with discounts for sustained throughput. By combining hardware selection, model optimization, and efficient deployment practices, developers can tailor inference costs to fit budgets ranging from small-scale prototypes to enterprise applications.