AI Models
DeepSeek-V4.1-Flash： Pushing the Limits of KV Cache Compression
DeepSeek-V4.1-Flash 发布：552B MoE 多模态模型主打 KV cache 压缩
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
arXiv.orgThe widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Together, these compute, storage, and bandwidth demands constitute the primary bottleneck to further lowering deployment costs. To address this challenge, we introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens. With its Causal Encoder-Decoder (CED) architecture, the model activates 16B parameters per token during decode but only 8B parameters during prefill, substantially improving cost efficiency for agentic workloads. To push the limits of KV cache compression, DeepSeek-V4.1-Flash combines cross-layer KV cache reuse in Compressed Sparse Attention 2 (CSA2) with FP4 KV caching. These designs reduce its global KV cache footprint (always in HBM) to 890 bytes per token, roughly 1/4 of the corresponding footprint of DeepSeek-V4-Flash. Further, through a dedicated deployment optimization known as SWA Bounded Replay, DeepSeek-V4.1-Flash reduces its persistent KV cache footprint (always on SSD or in host memory) to roughly 1/8 of that of DeepSeek-V4-Flash. Despite its much smaller KV cache footprint, the model delivers substantially better performance than the baseline. In addition, we streamline the DeepSeek-V4 architecture and introduce several efficient architectural extensions. We pretrain DeepSeek-V4.1-Flash on a multimodal corpus comprising 45T tokens and conduct comprehensive post-training, yielding strong performance across diverse text-based and multimodal agentic scenarios. Model checkpoints are available at https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash.
Open sourceRecommended because
This is worth tracking because it is a concrete model capability signal, not just a passing headline. The source preview points to a change in model capability, availability, benchmark behavior, or developer access. For builders and operators, "DeepSeek-V4.1-Flash： Pushing the Limits of KV Cache Compression" can be used as a checkpoint for model selection, product roadmaps, eval planning, and timing decisions. I keep this thread indexed so future searches around AI model updates, capability shifts, and developer adoption can land on a source-linked page instead of disappearing into a fast-moving feed from arXiv.org.
What to take from this signal
Context
"DeepSeek-V4.1-Flash： Pushing the Limits of KV Cache Compression" is archived here as a source-linked AI signal from arXiv.org. The useful part is the connection between DeepSeek-V4, 1-Flash, Pushing, Limits, Cache and model selection, product roadmaps, eval planning, and timing decisions, which makes the item more actionable than a normal feed headline. The source context says: The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Together, these compute, storage, and bandwidth demands constitute the primary bottleneck to further lowering deployment costs. To address this challenge, we introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens. With its Causal Encoder-Decoder (CED) architecture, the model activates 16B parameters per token during decode but only 8B parameters during prefill, substantially improving cost efficiency for agentic workloads. To push the limits of KV cache compression, DeepSeek-V4.1-Flash combines cross-layer KV cache reuse in Compressed Sparse Attention 2 (CSA2) with FP4 KV caching. These designs reduce its global KV cache footprint (always in HBM) to 890 bytes per token, roughly 1/4 of the corresponding footprint of DeepSeek-V4-Flash. Further, through a dedicated deployment optimization known as SWA Bounded Replay, DeepSeek-V4.1-Flash reduces its persistent KV cache footprint (always on SSD or in host memory) to roughly 1/8 of that of DeepSeek-V4-Flash. Despite its much smaller KV cache footprint, the model delivers substantially better performance than the baseline. In addition, we streamline the DeepSeek-V4 architecture and introduce several efficient architectural extensions. We pretrain DeepSeek-V4.1-Flash on a multimodal corpus comprising 45T tokens and conduct comprehensive post-training, yielding strong performance across diverse text-based and multimodal agentic scenarios. Model checkpoints are available at
Builder takeaway
For an AI builder, the main takeaway is to watch how this signal changes practical decisions around model quality, latency, cost, eval coverage, and release timing. It can inform what to test next, which product surface to compare, and whether the underlying workflow is ready for real users.
Source context
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Search angles
- DeepSeek-V4.1-Flash： Pushing the Limits of KV Cache Compression AI Models context
- arXiv.org AI model releases
- DeepSeek-V4, 1-Flash, Pushing, Limits, Cache builder takeaway
- AI model updates, capability shifts, and developer adoption
This page keeps a source preview and a stable archive URL for search discovery. The original source remains authoritative.