Search “Reasoning”

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.

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.

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.

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.

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.

Baseten becomes Postman's recommended inference partner for enterprise Agents

On October 30, 2026, Baseten became Postman's recommended inference partner for enterprise agents, responsible for providing production inference and post-training support for agents built through the Postman Agent Stack. In an engineering blog post, Baseten said that its seven model APIs released in 2026 all ranked in the top two in Artificial Analysis's throughput and latency rankings, with Beam 1.0, DeepSeek V4.1 Flash and others taking first place. The blog post also mentioned that Harvey used Baseten to post-train a 27B open-source model, matching closed-source frontier models on legal agent benchmarks, while the latter cost about $50 per task. There are currently no subsequent developments.

Baseten becomes Postman's recommended inference partner for enterprise Agents

On October 30, 2026, Baseten became Postman's recommended inference partner for enterprise agents, responsible for providing production inference and post-training support for agents built through the Postman Agent Stack. In an engineering blog post, Baseten said that its seven model APIs released in 2026 all ranked in the top two in Artificial Analysis's throughput and latency rankings, with Beam 1.0, DeepSeek V4.1 Flash and others taking first place. The blog post also mentioned that Harvey used Baseten to post-train a 27B open-source model, matching closed-source frontier models on legal agent benchmarks, while the latter cost about $50 per task. There are currently no subsequent developments.

View story:Baseten becomes Postman's recommended inference partner for enterprise Agents

DeepSeek open-sources 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.

View story:DeepSeek open-sources the inference engine DeepInfer on GitHub

Google upgrades LiteRT GPU inference in Play services

On October 28, 2026, the Google Developers Blog announced that LiteRT in Google Play services has received its biggest upgrade yet: GPU inference based on ML Drift is about 1.4 times faster than the old GPU delegate and is provided through the new CompiledModel API. After switching to this runtime, app size shrinks by about 8 MB, because the runtime is distributed uniformly with Play services and is no longer packaged into the APK. Currently, more than 100,000 Android apps already bundle LiteRT or TensorFlow Lite. The solution supports Android 7 and above, Wear OS 4+, and Android Automotive OS (Android 11+), while NPU support is still under development.

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.

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.

View story:Aravind Srinivas asks about reasoning tasks still bottlenecked by intelligence

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.

View story:Ant Ling and NVIDIA Optimize Single-GPU Inference for Ling-3.0-flash

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.

Google launches A2UI Express, a replaceable reasoning format for A2UI

On October 27, 2026, Google launched a replaceable reasoning format for A2UI, with A2UI Express becoming the new default format. According to the Google Developers Blog, Express can reduce latency by up to 3x and reduce the tokens needed to generate comparable UI by up to 3x; when used with decision models such as Jev for layout decisions, the latency reduction can reach 80x. The format has landed in the Python agent SDK (a2ui-agent-sdk 0.9.0), the TypeScript SDK (@a2ui/agent), and the Dart SDK (a2ui_agent 0.0.1-wip004), and developers can switch formats with a single parameter when creating a generator. There are currently no reports of further developments.

LiteRT in Google Play services gets GPU inference upgrade

On October 28, 2026, the Google Developers Blog announced that LiteRT in Google Play services has received its biggest upgrade yet: GPU inference based on ML Drift is about 1.4 times faster than the old GPU delegate and is provided through the new CompiledModel API. After switching to this runtime, app size shrinks by about 8 MB, because the runtime is distributed uniformly with Play services and is no longer packaged into the APK. Currently, more than 100,000 Android apps already bundle LiteRT or TensorFlow Lite. The solution supports Android 7 and above, Wear OS 4+, and Android Automotive OS (Android 11+), while NPU support is still under development.

View story:Google upgrades LiteRT GPU inference in Play services

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.

View story:OpenRouter announces CSAM screening for all inference requests

Google launches replaceable reasoning format A2UI Express for A2UI

On October 27, 2026, Google launched a replaceable reasoning format for A2UI, with A2UI Express becoming the new default format. According to the Google Developers Blog, Express can reduce latency by up to 3x and reduce the tokens needed to generate comparable UI by up to 3x; when used with decision models such as Jev for layout decisions, the latency reduction can reach 80x. The format has landed in the Python agent SDK (a2ui-agent-sdk 0.9.0), the TypeScript SDK (@a2ui/agent), and the Dart SDK (a2ui_agent 0.0.1-wip004), and developers can switch formats with a single parameter when creating a generator. There are currently no reports of further developments.

View story:Google launches A2UI Express, a replaceable reasoning format for A2UI

DeepSeek open-sources inference framework DeepInfer

On October 20, 2026, DeepSeek open-sourced its internal inference framework DeepInfer. According to Yuchen Jin, citing data disclosed by DeepSeek, a single cluster processes 155 trillion tokens per day and generates $38 in revenue per GPU-hour (off-peak pricing); DeepInfer supports both NVIDIA Blackwell and Huawei Ascend. A subsequent 36Kr report stated that its accompanying open-source deepseek-recipe supports NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. On October 21, swyx, citing Latent.Space data, said a single cluster processes 155T tokens per day with a 96% input cache hit rate, and argued that the lifetime value of each sold GPU has risen by about 30%. On October 24, Nathan Lambert said DeepSeek's single cluster processes 150T tokens per day, exceeding the combined open-source model inference volume of OpenRouter, Fireworks, Together, and BaseTen, and noted that the actual number of clusters is unknown; on the same day, LMSYS published a same-hardware benchmark comparison, stating that SGLang meets or exceeds DeepInfer on DeepInfer's own benchmark, with prefill throughput up to 1.04x and decode step length shortened by 1.1%–7.4% on 64x GB300.

Raschka shares an eight-part video course on implementing reasoning models from scratch

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