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OpenRouter announces CSAM screening for all inference requests
On October 27, 2026, OpenRouter announced that it will conduct child sexual abuse material (CSAM) screening on all inference requests during request processing. According to its announcement, the screening includes comparing content against hashes of known CSAM, and will also retain suspected content that does not match a hash but is flagged by classifiers; content flagged as suspected CSAM may be retained for manual review and reported and preserved in accordance with the law. The announcement also states that this screening applies equally to requests covered by OpenRouter's zero data retention (ZDR) policy, and that ZDR does not exempt the obligations to report and preserve data in accordance with the law. There are currently no subsequent developments.
On October 27, 2026, OpenRouter announced that it will conduct child sexual abuse material (CSAM) screening on all inference requests during request processing. According to its announcement, the screening includes comparing content against hashes of known CSAM, and will also retain suspected content that does not match a hash but is flagged by classifiers; content flagged as suspected CSAM may be retained for manual review and reported and preserved in accordance with the law. The announcement
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OpenRouter announces CSAM screening for all inference requests
On October 27, 2026, OpenRouter announced that it will conduct child sexual abuse material (CSAM) screening on all inference requests during request processing. According to its announcement, the screening includes comparing content against hashes of known CSAM, and will also retain suspected content that does not match a hash but is flagged by classifiers; content flagged as suspected CSAM may be retained for manual review and reported and preserved in accordance with the law. The announcement also states that this screening applies equally to requests covered by OpenRouter's zero data retention (ZDR) policy, and that ZDR does not exempt the obligations to report and preserve data in accordance with the law. There are currently no subsequent developments.
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View story:OpenRouter announces CSAM screening for all inference requests
MiniMax thanks for bringing H3 to TPU and looks forward to open-source work on efficient video inference
MiniMax responded to SGLang including MiniMax H3 in the TPU support list of SGLang-TorchTPU, expressing thanks and saying it looks forward to more open-source work around efficient video inference. SGLang said that SGLang-TorchTPU was jointly built by SGLang, the Google TPU team, and the Meta PyTorch team, has been used for Meta's production inference, and supports models including Muse Spark Open, GLM-5.3 Flash, Kimi K3, Ling 3.1 Flash, and MiniMax H3. There are currently no facts about subsequent developments.
MiniMax responded to SGLang including MiniMax H3 in the TPU support list of SGLang-TorchTPU, expressing thanks and saying it looks forward to more open-source work around efficient video inference. SGLang said that SGLang-TorchTPU was jointly built by SGLang, the Google TPU team, and the Meta PyTorch team, has been used for Meta's production inference, and supports models including Muse Spark Open, GLM-5.3 Flash, Kimi K3, Ling 3.1 Flash, and MiniMax H3. There are currently no facts about subsequ
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MiniMax thanks for bringing H3 to TPU and looks forward to efficient video inference open-source work
MiniMax responded to SGLang including MiniMax H3 in the TPU support list of SGLang-TorchTPU, expressing thanks and saying it looks forward to more open-source work around efficient video inference. SGLang said that SGLang-TorchTPU was jointly built by SGLang, the Google TPU team, and the Meta PyTorch team, has been used for Meta's production inference, and supports models including Muse Spark Open, GLM-5.3 Flash, Kimi K3, Ling 3.1 Flash, and MiniMax H3. There are currently no facts about subsequent developments.
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DeepSeek open-sources inference framework DeepInfer
On October 20, 2026, DeepSeek announced the open-sourcing of its internal production-grade inference framework, DeepInfer. According to Latent Space, the framework powers DeepSeek-V4.1-Flash, is customized based on vLLM, supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and targets the NVIDIA SM100 and Huawei Ascend 950 platforms; the published single-cluster production figures are 155 trillion tokens processed per day, a 96% input cache hit rate, 400 ms first-token latency, and a generation speed of 230 tokens/s. The report also noted that components such as KV cache management and load balancing have not yet been fully open-sourced. The next day, DeepSeek published the DeepInfer technical report on Zhihu, disclosing internal details including the hardware selection for Prefill and Decode and the KVCache targets, and providing data showing a single cluster serving 155 trillion Tokens/day at $38 per card per hour at off-peak prices. The framework is currently open-sourced, but the KV cache system, Prefill/Decode disaggregation, and load balancing required for a complete service are not fully included in this open-source project.
On October 20, 2026, DeepSeek announced the open-sourcing of its internal production-grade inference framework, DeepInfer. According to Latent Space, the framework powers DeepSeek-V4.1-Flash, is customized based on vLLM, supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and targets the NVIDIA SM100 and Huawei Ascend 950 platforms; the published single-cluster production figures are 155 trillion tokens processed per day, a 96% input cache hit rate, 4
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DeepSeek open-sources inference framework DeepInfer
On October 20, 2026, DeepSeek announced the open-sourcing of its internal production-grade inference framework, DeepInfer. According to Latent Space, the framework powers DeepSeek-V4.1-Flash, is customized based on vLLM, supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and targets the NVIDIA SM100 and Huawei Ascend 950 platforms; the published single-cluster production figures are 155 trillion tokens processed per day, a 96% input cache hit rate, 400 ms first-token latency, and a generation speed of 230 tokens/s. The report also noted that components such as KV cache management and load balancing have not yet been fully open-sourced. The next day, DeepSeek published the DeepInfer technical report on Zhihu, disclosing internal details including the hardware selection for Prefill and Decode and the KVCache targets, and providing data showing a single cluster serving 155 trillion Tokens/day at $38 per card per hour at off-peak prices. The framework is currently open-sourced, but the KV cache system, Prefill/Decode disaggregation, and load balancing required for a complete service are not fully included in this open-source project.
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View story:DeepSeek open-sources inference framework DeepInfer
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X.PIN Announces Benchmark Test Results for Iluvatar CoreX Tiange 300
On October 30, 2026, X.PIN relayed and published benchmark test data for the Iluvatar CoreX Tiange 300. Its report claimed that in standard inference tests, the Tiange 300's throughput was 1.05 times that of the Nvidia H200; under MoE workloads, performance improved by 15% and first-token latency decreased by 30%; and when fine-tuning Qwen3-32B, its speed was 1.33 times that of the H100. There are currently no follow-up developments or official responses.
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