English translation is being prepared.
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Matched fieldsSummary
Hao AI Lab integrated Pika Labs' open-source MiniMax H3 Flex into FastVideo, achieving inference speeds up to 7 times that of the official open-source inference code. On a single GB200, generating a 5-second 480p video with audio using the same prompt and random seed took 59.7 seconds with the official code, 26.6 seconds with FastVideo BF16, 16.8 seconds with NVFP4 QAD, and 15.2 seconds with FP4 attention. The official code only supports a single GPU; after FastVideo added multi-GPU support, generating a 15-second video on 4 GPUs was 7 times faster; generating a 5-second 480p first-and-last-frame video on a single DGX Spark took 59.5 seconds. The project also releases the weights, inference code, and complete training recipe, and supports the 2K model of Open-H3-Regenerate-2K.
Hao AI Lab integrated Pika Labs' open-source MiniMax H3 Flex into FastVideo, achieving inference speeds up to 7 times that of the official open-source inference code. On a single GB200, generating a 5-second 480p video with audio using the same prompt and random seed took 59.7 seconds with the official code, 26.6 seconds with FastVideo BF16, 16.8 seconds with NVFP4 QAD, and 15.2 seconds with FP4 attention. The official code only supports a single GPU; after FastVideo added multi-GPU support, gen
Matched fieldsBody
Hao AI Lab has integrated Pika Labs' open-source MiniMax H3 Flex into FastVideo. The official open-source inference code takes 59.7 seconds to generate a 5-second 480p video with audio on a single GB200, while FastVideo BF16 takes 26.6 seconds, NVFP4 QAD takes 16.8 seconds, and FP4 attention takes 15.2 seconds. The team claims it is up to 7 times faster than the official code, and has added multi-GPU support, generating a 15-second clip 7 times faster on 4 GPUs. NVFP4 QAD distills the BF16 Flex model, coming 14-17% closer to BF16 than ordinary quantization; FP4 attention reduces a 768p 15-second clip from 101.8 seconds to 85.3 seconds. This clip takes 59.5 seconds on a single DGX Spark. FastVideo also supports the 2K model of Open-H3-Regenerate-2K, and the weights, inference code, and complete training recipe have all been released.
Hao AI Lab has integrated Pika Labs' open-source MiniMax H3 Flex into FastVideo. The official open-source inference code takes 59.7 seconds to generate a 5-second 480p video with audio on a single GB200, while FastVideo BF16 takes 26.6 seconds, NVFP4 QAD takes 16.8 seconds, and FP4 attention takes 15.2 seconds. The team claims it is up to 7 times faster than the official code, and has added multi-GPU support, generating a 15-second clip 7 times faster on 4 GPUs. NVFP4 QAD distills the BF16 Flex
Matched fieldsBody
Cohere has launched third-party open models in Model Vault and Model Vault Encrypted, allowing enterprises to run both Cohere's own models and open models such as DeepSeek V4.1 Flash, GLM 5.3 Flash, Gemma 4, GPT-OSS, and Phi on a fully managed encrypted inference platform. Model Vault applies zero data retention to prompts and outputs by default, while the Encrypted version uses confidential computing so that neither Cohere nor cloud providers can see the data. Model Vault is now available in the United States, the European Union, and Canada; the Encrypted version is currently limited to the United States, with support for the European Union and Canada coming soon.

Cohere has launched third-party open models in Model Vault and Model Vault Encrypted, allowing enterprises to run both Cohere's own models and open models such as DeepSeek V4.1 Flash, GLM 5.3 Flash, Gemma 4, GPT-OSS, and Phi on a fully managed encrypted inference platform. Model Vault applies zero data retention to prompts and outputs by default, while the Encrypted version uses confidential computing so that neither Cohere nor cloud providers can see the data. Model Vault is now available in th
Matched fieldsBody
AWS partner Cartesia provides voice models on AWS based on State Space Models (SSMs) with first-byte latency under 100ms: Sonic text-to-speech produces its first audio in about 90ms, supports more than 44 languages and voice cloning from 10-second audio samples, and Ink speech-to-text supports native turn detection and streaming transcription. SSMs achieve native streaming inference through fixed-size hidden states and token-by-token processing, avoiding the latency that comes from Transformers processing an entire sequence.

AWS partner Cartesia provides voice models on AWS based on State Space Models (SSMs) with first-byte latency under 100ms: Sonic text-to-speech produces its first audio in about 90ms, supports more than 44 languages and voice cloning from 10-second audio samples, and Ink speech-to-text supports native turn detection and streaming transcription. SSMs achieve native streaming inference through fixed-size hidden states and token-by-token processing, avoiding the latency that comes from Transformers
Matched fieldsBody
Sarvam AI has released Sarvam Prism, a System One decision model based on Sarvam 105B, which achieves an average score of 71.8 with a median response time of 44 ms on B200 GPUs, the highest score within 100 ms among the 25 decision models it tested. At confidence levels above 90%, the model's actual error rate is lower than expected: 2.2% versus 3.3% for English and 4.4% versus 5.4% for Indic languages, and it keeps its answers unchanged in a 2,000-question option-shuffling test. The weights are open-sourced under the Apache 2.0 license on Hugging Face, requiring at least 120 GB of VRAM on a single GPU to run in FP8, and it is also available via the Sarvam API.

Sarvam AI has released Sarvam Prism, a System One decision model based on Sarvam 105B, which achieves an average score of 71.8 with a median response time of 44 ms on B200 GPUs, the highest score within 100 ms among the 25 decision models it tested. At confidence levels above 90%, the model's actual error rate is lower than expected: 2.2% versus 3.3% for English and 4.4% versus 5.4% for Indic languages, and it keeps its answers unchanged in a 2,000-question option-shuffling test. The weights are
Matched fieldsBody
Li Wei, a professor at Cheung Kong Graduate School of Business, analyzes AI infrastructure financing risks, noting that Anthropic finalized approximately $42 billion in syndicated loans and about $18 billion in subordinated debt through the "Project Polaris," with Broadcom providing a guarantee and Blackstone contributing half of the funding. The article also uses Meta's Hyperion data center as an example, explaining that it moved $27 billion in debt off its balance sheet through an SPV, with a debt-to-asset ratio of about 90%, higher than Meta's own ratio of about 43%. The author believes there is insufficient evidence to assert that a Minsky moment is imminent, but signs such as rising issuance of junk-rated data center bonds and falling prices of some bonds are worth watching.
Li Wei, a professor at Cheung Kong Graduate School of Business, analyzes AI infrastructure financing risks, noting that Anthropic finalized approximately $42 billion in syndicated loans and about $18 billion in subordinated debt through the "Project Polaris," with Broadcom providing a guarantee and Blackstone contributing half of the funding. The article also uses Meta's Hyperion data center as an example, explaining that it moved $27 billion in debt off its balance sheet through an SPV, with a
Matched fieldsBody
An AWS official blog post explains how to fine-tune a shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD): using GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, using the ShoppingBench scorer to retain only trajectories that solve the task, and then performing serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the distilled 4B student's average absolute success rate increased from 0.297 to 0.541, essentially on par with the teacher model and mid-sized frontier models; when the teacher's reasoning process was removed and only tool calls were learned, the average success rate dropped to 0.415.

An AWS official blog post explains how to fine-tune a shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD): using GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, using the ShoppingBench scorer to retain only trajectories that solve the task, and then performing serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the distilled 4B student's average absolute success rate increased from 0.297 to 0.541, essentia
Matched fieldsBody
The LMSYS blog introduces how HiCache is implemented in context parallelism, pipeline parallelism, speculative decoding, unified KV layout, and P/D disaggregation scenarios, with the core being to keep cache decisions consistent across ranks. The iFlytek Spark MaaS team used HiCache plus external storage to serve GLM-5.1/5.2 CodingPlan workloads, raising the cache hit rate from about 30% to 95% and reducing median TTFT to under 3 seconds. In CP sharding tests, the GPU token hit rate increased by 40.48 percentage points, the host memory serving token volume dropped by 81.2%, and the average prefill queueing time decreased by 56.2%.

The LMSYS blog introduces how HiCache is implemented in context parallelism, pipeline parallelism, speculative decoding, unified KV layout, and P/D disaggregation scenarios, with the core being to keep cache decisions consistent across ranks. The iFlytek Spark MaaS team used HiCache plus external storage to serve GLM-5.1/5.2 CodingPlan workloads, raising the cache hit rate from about 30% to 95% and reducing median TTFT to under 3 seconds. In CP sharding tests, the GPU token hit rate increased by
Matched fieldsBody