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Amazon Bedrock adds in-India inference for Kimi K3
On November 4, 2026, Amazon Bedrock added an in-India inference option alongside its existing global and US-based inference. When calling in.moonshotai.kimi-k3, requests are processed only in the Mumbai and Hyderabad Regions to meet strict data residency requirements. An article published the same day by AWS Machine Learning also mentioned that Ultrahuman has already been using the model in production, leveraging prompt caching to reduce LLM costs by 80% and speed up multi-turn conversations by 85%, and provided OCR sample code that uses Kimi K3 to read scanned Hindi documents and follow up in Hinglish. There are currently no subsequent developments.
On November 4, 2026, Amazon Bedrock added an in-India inference option alongside its existing global and US-based inference. When calling in.moonshotai.kimi-k3, requests are processed only in the Mumbai and Hyderabad Regions to meet strict data residency requirements. An article published the same day by AWS Machine Learning also mentioned that Ultrahuman has already been using the model in production, leveraging prompt caching to reduce LLM costs by 80% and speed up multi-turn conversations by
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Amazon Bedrock adds in-India inference for Kimi K3
On November 4, 2026, Amazon Bedrock added an in-India inference option alongside its existing global and US-based inference. When calling in.moonshotai.kimi-k3, requests are processed only in the Mumbai and Hyderabad Regions to meet strict data residency requirements. An article published the same day by AWS Machine Learning also mentioned that Ultrahuman has already been using the model in production, leveraging prompt caching to reduce LLM costs by 80% and speed up multi-turn conversations by 85%, and provided OCR sample code that uses Kimi K3 to read scanned Hindi documents and follow up in Hinglish. There are currently no subsequent developments.
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View story:Amazon Bedrock adds in-India inference for Kimi K3
inclusionAI open-sources the dInfer diffusion language model inference framework
On October 10, 2025, Ant inclusionAI open-sourced the first version of dInfer, an inference framework for diffusion language models (dLLM). According to the introduction of its new GitHub repository, the framework splits inference into four modules: the model, the diffusion iteration manager, the decoder, and the KV-cache manager, and it supports batched inference and is compatible with the LLaDA, LLaDA-MoE, and LLaDA2 series. In terms of performance, on a single 8×H800 node, LLaDA-MoE exceeds 1,100 TPS on HumanEval at batch size 1, averages 800+ TPS across six benchmarks, and is 10 times faster than Fast-dLLM while maintaining accuracy. There are currently no public facts regarding further development progress.
On October 10, 2025, Ant inclusionAI open-sourced the first version of dInfer, an inference framework for diffusion language models (dLLM). According to the introduction of its new GitHub repository, the framework splits inference into four modules: the model, the diffusion iteration manager, the decoder, and the KV-cache manager, and it supports batched inference and is compatible with the LLaDA, LLaDA-MoE, and LLaDA2 series. In terms of performance, on a single 8×H800 node, LLaDA-MoE exceeds 1
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inclusionAI open-sources the dInfer diffusion language model inference framework
On October 10, 2025, Ant inclusionAI open-sourced the first version of dInfer, an inference framework for diffusion language models (dLLM). According to the introduction of its new GitHub repository, the framework splits inference into four modules: the model, the diffusion iteration manager, the decoder, and the KV-cache manager, and it supports batched inference and is compatible with the LLaDA, LLaDA-MoE, and LLaDA2 series. In terms of performance, on a single 8×H800 node, LLaDA-MoE exceeds 1,100 TPS on HumanEval at batch size 1, averages 800+ TPS across six benchmarks, and is 10 times faster than Fast-dLLM while maintaining accuracy. There are currently no public facts regarding further development progress.
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View story:inclusionAI open-sources the dInfer diffusion language model inference framework
Hao AI Lab open-sources the JetSpec++ model, code, and inference engine
On October 31, 2026, the UCSD Hao AI Lab announced that it would fully open-source the JetSpec++ model, code, and inference engine, along with links to the paper, code, and Hugging Face. This speculative decoding framework pipelines draft generation and verification through causal parallel drafting, a hidden-state predictor, and an anchor-token decoder. According to the data it published, on Qwen3-8B, JetSpec++ achieves 1,361 TPS, delivering up to 1.54x end-to-end speedup relative to DSpark and 7.45x relative to autoregressive decoding, while remaining lossless; after combining NVFP4 with kernel optimizations, on two GB200 GPUs, Qwen3-8B exceeds 4,000 TPS and Qwen3.8-27B exceeds 900 TPS. There are currently no further developments.
On October 31, 2026, the UCSD Hao AI Lab announced that it would fully open-source the JetSpec++ model, code, and inference engine, along with links to the paper, code, and Hugging Face. This speculative decoding framework pipelines draft generation and verification through causal parallel drafting, a hidden-state predictor, and an anchor-token decoder. According to the data it published, on Qwen3-8B, JetSpec++ achieves 1,361 TPS, delivering up to 1.54x end-to-end speedup relative to DSpark and
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Hao AI Lab open-sources the JetSpec++ model, code, and inference engine
On October 31, 2026, the UCSD Hao AI Lab announced that it would fully open-source the JetSpec++ model, code, and inference engine, along with links to the paper, code, and Hugging Face. This speculative decoding framework pipelines draft generation and verification through causal parallel drafting, a hidden-state predictor, and an anchor-token decoder. According to the data it published, on Qwen3-8B, JetSpec++ achieves 1,361 TPS, delivering up to 1.54x end-to-end speedup relative to DSpark and 7.45x relative to autoregressive decoding, while remaining lossless; after combining NVFP4 with kernel optimizations, on two GB200 GPUs, Qwen3-8B exceeds 4,000 TPS and Qwen3.8-27B exceeds 900 TPS. There are currently no further developments.
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View story:Hao AI Lab open-sources the JetSpec++ model, code, and inference engine
DeepSeek open-sources the inference engine DeepInfer on GitHub
On October 29, 2026, DeepSeek open-sourced the inference engine DeepInfer on GitHub; this engine is the inference engine behind DeepSeek-V4.1-Flash. Leiphone reported the same day that DeepInfer includes technologies such as MoE expert parallelism, asynchronous batch scheduling, custom kernels, and speculative decoding, and disclosed that a single cluster needs to process approximately 155 trillion tokens per day. Its Zero-Overhead Batch Scheduler lets the CPU schedule computation one round in advance, overlapping with GPU forward passes, and handles cross-round state misalignment through total_inflight_tokens accounting, Remap mapping, and DeferredRelease deferred reclamation. Currently, only offline inference capabilities are open; components such as online KV Cache storage and cross-replica load balancing have not yet been fully disclosed, and the official team has not separately published end-to-end speedup data for this scheduling mechanism relative to synchronous solutions.
On October 29, 2026, DeepSeek open-sourced the inference engine DeepInfer on GitHub; this engine is the inference engine behind DeepSeek-V4.1-Flash. Leiphone reported the same day that DeepInfer includes technologies such as MoE expert parallelism, asynchronous batch scheduling, custom kernels, and speculative decoding, and disclosed that a single cluster needs to process approximately 155 trillion tokens per day. Its Zero-Overhead Batch Scheduler lets the CPU schedule computation one round in a
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vLLM Blog Publishes Technical Retrospective on Kimi K3 Inference Optimization
On October 16, 2026, the vLLM blog published a technical retrospective titled "Hyper Optimizing Kimi K3 in vLLM: Moving Fast and Responsibly." According to a TMTPost·AGI report, NVIDIA CentML engineer Benjamin Chislett used a Megakernel written in 12,000 lines of CuTe DSL to optimize inference for Moonshot AI's Kimi K3; the vLLM blog reports over 840 tokens/s at batch 1 and a 1.5x to 2x improvement in decoding throughput across multiple configurations, while SemiAnalysis estimates 423 tok/s/user on GB300 NVL72, a 1.68x improvement. The vLLM blog states that agent-optimized inference code is now outpacing human review and maintenance capacity, and the team therefore proposed a fast-track model optimization RFC so that reviewers judge designs from high-level abstractions rather than reviewing code line by line. The report also mentions that NVIDIA is developing optimizations for other models in vLLM, such as DeepSeek V4.1.
On October 16, 2026, the vLLM blog published a technical retrospective titled "Hyper Optimizing Kimi K3 in vLLM: Moving Fast and Responsibly." According to a TMTPost·AGI report, NVIDIA CentML engineer Benjamin Chislett used a Megakernel written in 12,000 lines of CuTe DSL to optimize inference for Moonshot AI's Kimi K3; the vLLM blog reports over 840 tokens/s at batch 1 and a 1.5x to 2x improvement in decoding throughput across multiple configurations, while SemiAnalysis estimates 423 tok/s/user
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Guillermo Rauch announces that Vercel is partnering with Particle to provide models and inference
On October 29, 2026, Guillermo Rauch stated that Vercel has reached a partnership with Suhail to provide Particle's models and inference services through the Vercel AI Gateway. He said Particle's goal is to make intelligence as ubiquitous and abundant as electricity, and the two sides will work together to achieve this goal for developers and enterprises. Currently, this partnership has only this one public statement, and no subsequent progress has been disclosed.
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Guillermo Rauch announces that Vercel is partnering with Particle to provide models and inference
On October 29, 2026, Guillermo Rauch stated that Vercel has reached a partnership with Suhail to provide Particle's models and inference services through the Vercel AI Gateway. He said Particle's goal is to make intelligence as ubiquitous and abundant as electricity, and the two sides will work together to achieve this goal for developers and enterprises. Currently, this partnership has only this one public statement, and no subsequent progress has been disclosed.
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Sam Altman discusses the DRAM utilization issue in local inference
On October 30, 2026, OpenAI CEO Sam Altman put forward a view on the podcast "Cheeky Pint": having everyone leave globally scarce DRAM memory idle in personal computers at only 5% utilization is quite bad from the perspective of global resource allocation; memory should be concentrated in cloud data centers to achieve maximum utilization. He also called the progress in resolving the memory shortage "terrible," arguing that memory manufacturers are conservative in capital expenditure due to historical loss cycles, and that the "invisible hand" is failing. There are currently no subsequent developments.
On October 30, 2026, OpenAI CEO Sam Altman put forward a view on the podcast "Cheeky Pint": having everyone leave globally scarce DRAM memory idle in personal computers at only 5% utilization is quite bad from the perspective of global resource allocation; memory should be concentrated in cloud data centers to achieve maximum utilization. He also called the progress in resolving the memory shortage "terrible," arguing that memory manufacturers are conservative in capital expenditure due to histo
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Sam Altman on the DRAM utilization problem in local inference
On October 30, 2026, OpenAI CEO Sam Altman put forward a view on the podcast "Cheeky Pint": having everyone leave globally scarce DRAM memory idle in personal computers at only 5% utilization is quite bad from the perspective of global resource allocation; memory should be concentrated in cloud data centers to achieve maximum utilization. He also called the progress in resolving the memory shortage "terrible," arguing that memory manufacturers are conservative in capital expenditure due to historical loss cycles, and that the "invisible hand" is failing. There are currently no subsequent developments.
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View story:Sam Altman discusses the DRAM utilization issue in local inference
Yuchen Jin laments that a year ago, image model inference engines were all handwritten
Yuchen Jin shared a conversation he had with a friend: a year ago, the ultra-fast inference engine they built for image models was entirely handwritten. He lamented about this, "Holy shit. Just ONE year ago." There is currently no further follow-up.
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Aravind Srinivas asks about reasoning tasks still bottlenecked by intelligence
On October 29, 2026, Perplexity CEO Aravind Srinivas posted a question: What reasoning tasks are people still stuck on at the level of intelligence, rather than compute or funding? The question shifts the focus to intelligence itself rather than compute or cost constraints; no follow-up developments or responses have been recorded so far.
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Ant Ling and NVIDIA Optimize Single-GPU Inference for Ling-3.0-flash
On October 29, 2026, Ant Ling, in collaboration with the NVIDIA AI team, optimized the inference of the open-weight model Ling-3.0-flash on a single NVIDIA DGX Spark using three techniques. On 1,319 GSM8K problems, decoding throughput increased from 37.81 tok/s to 53.89 tok/s (+42.5%), and average latency dropped from 15.21s to 11.49s (-24.5%), with no statistically detectable change in accuracy. There are currently no further developments.
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vLLM Blog Publishes Technical Retrospective on Kimi K3 Inference Optimization
On October 16, 2026, the vLLM blog published a technical retrospective titled "Hyper Optimizing Kimi K3 in vLLM: Moving Fast and Responsibly." According to a TMTPost·AGI report, NVIDIA CentML engineer Benjamin Chislett used a Megakernel written in 12,000 lines of CuTe DSL to optimize inference for Moonshot AI's Kimi K3; the vLLM blog reports over 840 tokens/s at batch 1 and a 1.5x to 2x improvement in decoding throughput across multiple configurations, while SemiAnalysis estimates 423 tok/s/user on GB300 NVL72, a 1.68x improvement. The vLLM blog states that agent-optimized inference code is now outpacing human review and maintenance capacity, and the team therefore proposed a fast-track model optimization RFC so that reviewers judge designs from high-level abstractions rather than reviewing code line by line. The report also mentions that NVIDIA is developing optimizations for other models in vLLM, such as DeepSeek V4.1.
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View story:vLLM Blog Publishes Technical Retrospective on Kimi K3 Inference Optimization
Yuchen Jin laments that a year ago image model inference engines were all handwritten
Yuchen Jin shared a conversation he had with a friend: a year ago, the ultra-fast inference engine they built for image models was entirely handwritten. He lamented about this, "Holy shit. Just ONE year ago." There is currently no further follow-up.
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View story:Yuchen Jin laments that a year ago, image model inference engines were all handwritten
Aravind Srinivas asks about reasoning tasks still bottlenecked by intelligence
On October 29, 2026, Perplexity CEO Aravind Srinivas posted a question: What reasoning tasks are people still stuck on at the level of intelligence, rather than compute or funding? The question shifts the focus to intelligence itself rather than compute or cost constraints; no follow-up developments or responses have been recorded so far.
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View story:Aravind Srinivas asks about reasoning tasks still bottlenecked by intelligence