DeepSeek-V4.1-Flash Packs 552B Parameters With Efficient MoE Inference
Overview
DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model from deepseek-ai that accepts text and images and generates text. Its defining feature is memory and inference efficiency for long, input-heavy workloads: it has 552B backbone parameters but activates 8B parameters per token during prefill and 16B during decoding, supports a context window of up to 1M tokens, and reduces its global KV-cache footprint to 890 bytes per token. The model uses a 40-layer Causal Encoder-Decoder architecture with 20 causal-encoder layers and 20 decoder layers, plus compressed sparse attention, FP4 KV caching, conditional memory, and speculative decoding. It was trained from scratch on a 45T-token multimodal corpus. The repository identifies transformers as the library and image-text-to-text as the pipeline type, but the release does not include a Jinja chat template; production integration requires the supplied Python encoding reference or the separate deepseek-recipe toolkit. The most important practical qualification is that the model is...
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