According to DeepSeek, the DeepSeek-V3/R1 Inference System has been optimized for improved throughput and latency, which could significantly impact trading algorithms relying on quick data processing. The system utilizes cross-node EP-powered batch scaling, computation-communication overlap, and load balancing techniques. This optimization is crucial for traders who depend on high-frequency trading algorithms that require rapid data processing and decision-making capabilities.

According to DeepSeek (@deepseek_ai), the Fire-Flyer File System (3FS) has achieved an impressive 6.6 TiB/s aggregate read throughput across a 180-node cluster, leveraging the full bandwidth potential of modern SSDs and RDMA networks. This performance enhancement could significantly impact trading systems that require rapid data access and processing. The system also achieves 3.66 TiB/min, providing high-speed data handling capabilities essential for real-time trading analytics and decision-making.

According to DeepSeek, the introduction of DualPipe and EPLB offers advanced solutions for V3/R1 training optimization. DualPipe is a bidirectional pipeline parallelism algorithm that improves computation-communication overlap, crucial for efficient training processes. Meanwhile, EPLB serves as an expert-parallel load balancer, ensuring optimal resource distribution during V3/R1 training. These tools are pivotal for traders looking to leverage high-performance computing in crypto market analysis (source: DeepSeek).

According to DeepSeek (@deepseek_ai), the DeepSeek API Platform is offering significant off-peak discounts starting today. Traders can benefit from a 50% discount on DeepSeek-V3 and a 75% discount on DeepSeek-R1 during off-peak hours from 16:30 to 00:30 UTC daily. This presents a strategic opportunity for traders to optimize resource usage and reduce costs during these specific hours.

According to @deepseek_ai, the newly introduced DeepGEMM library supports both dense and MoE GEMMs, achieving up to 1350+ FP8 TFLOPS on Hopper GPUs. This advancement is significant for V3/R1 training and inference, offering traders insights into potential hardware investments and performance efficiencies in AI-driven trading algorithms. The library is designed to be lightweight with no heavy dependencies, which is crucial for optimizing trading software infrastructure. Furthermore, its fully Just-In-Time compiled nature enhances performance, relevant for high-frequency trading applications.

According to DeepSeek, DeepEP is the first open-source EP communication library designed for MoE (Mixture of Experts) model training and inference. The library features efficient and optimized all-to-all communication capabilities, and offers both intranode and internode support utilizing NVLink and RDMA technologies. This development could enhance model training performance, potentially influencing trading strategies relying on AI model predictions (DeepSeek, 2025).

According to DeepSeek, the new FlashMLA kernel for Hopper GPUs, optimized for variable-length sequences, is now in production, offering BF16 support and achieving 3000 GB/s memory-bound and 580 TFLOPS, which can significantly boost computational efficiency in crypto trading algorithms.

According to DeepSeek AI (@deepseek_ai), the team is set to open-source five repositories next week as part of their efforts in exploring Artificial General Intelligence (AGI). This initiative is part of #OpenSourceWeek and aims to provide transparency in their developmental progress. Traders interested in AI technology investments should monitor this release as it could influence market sentiment and potential valuations in the AI sector.

According to DeepSeek, the NSA (Natively Trainable Sparse Attention) mechanism is designed to improve ultra-fast long-context training and inference capabilities through dynamic hierarchical sparse strategy, coarse-grained token compression, and fine-grained token selection, potentially enhancing trading algorithms by increasing processing efficiency and reducing computational load.

According to DeepSeek (@deepseek_ai), the recommended settings for deploying DeepSeek-R1 include no system prompt and a temperature of 0.6, which are crucial for optimal performance. Traders utilizing AI models in cryptocurrency trading should consider these settings to enhance decision-making accuracy and efficiency. The guidelines provided by DeepSeek, including official prompts for search and file uploads, aim to prevent model bypass, ensuring reliable trading insights. Source: [DeepSeek Twitter](https://twitter.com/deepseek_ai/status/1890324295181824107?ref_src=twsrc%5Etfw).

According to @OriolVinyalsML, distillation techniques, which have been in the spotlight due to @deepseek_ai, could influence cryptocurrency trading algorithms by enhancing model efficiency. Despite its initial rejection from NeurIPS 2014 for lacking novelty and impact, the technique's evolution suggests potential for improving algorithmic trading strategies by refining AI model predictions and reducing computational costs, making it relevant for traders seeking edge through advanced AI integration.

According to DeepSeek, there is a warning against trusting information from a specific impersonation account, highlighting the importance of verifying sources for accurate trading decisions.

According to DeepSeek, the official account on Twitter/X has warned traders about impersonation risks, urging vigilance to avoid being misled. This alert is crucial for traders relying on accurate information from verified sources to make informed decisions in the cryptocurrency markets.