AI news daily: what changed on October 29, 2026

1. pruned NVIDIA's new 30B speech LLM from 67.5GB to 10GB with zero retraining, it still transcribes at 5.16%

pruned NVIDIA's new 30B speech LLM from 67.5GB to 10GB with zero retraining, it still transcribes at 5.16% was reported by Reddit r/LocalLLaMA. See the original report for details.

Source: Reddit r/LocalLLaMA

2. [Update Benchmark] Basalt's new PRs (Strata fork) new record IQ3_S Qwen3.8 Flash Next on single RTX 5080 and

Basalt's new PRs (Strata fork) set a record for IQ3_S Qwen3.8 Flash on a single RTX 5080. Code and tool calls achieved up to 275 tok/s, while reasoning traces reached 190 tok/s. The setup included an RTX 5080 16GB PCIe gen5x16, 64 DDR5 6400, and a Core Ultra 9 285K. Some optimizations under the author's name remain pending, but upstream improvements include memory optimizations. The IQ3_S model is described as "super usable." The project is hosted on GitHub at github.com/jesdga95/basalt. Submitted by /u/yehyakar.

Source: Reddit r/LocalLLaMA

3. yeasah/Qwen3.8-27B-exl3 quants - lowest KLD of 0.0002

A new EXL3 quantized model named yeasah/Qwen3.8-27B-exl3 was shared, claiming the lowest KLD value of 0.0002. It uses 18.8 GiB VRAM for weights, according to the post. Comparisons show higher KLD values for other models like Unsloth Q8_K_XL (0.0005) and Minachist INT8 AutoRound (0.00056). The file size for this model is 23 GB, similar to turboderp's 6bpw SC variant. The post highlights its efficiency in achieving low KLD with reduced VRAM requirements. The summary includes data from Hugging Face and Reddit comments. No pricing or rankings are mentioned. The model's performance is presented as notable within the context of quantization techniques.

Source: Reddit r/LocalLLaMA

4. Qwen4 1M context window

A new version of the Qwen4 model series will feature a 1 million context window, according to a Reddit post. The Qwen4-27B model was not explicitly mentioned in the announcement but is expected to be revealed at Apsara Netherlands tomorrow. The release date for Qwen4-max was initially stated as November 1st but was later edited to October 1st, suggesting a placeholder date. The information was shared on the r/LocalLLaMA subreddit with a linked PR and image. No further details about the models or capabilities were provided beyond the context window size. The post was submitted by user /u/brainExploded99. No official confirmation of the date changes or model specifics has been released. The source includes a GitHub PR link and a screenshot of the discussion.

Source: Reddit r/LocalLLaMA

5. 5 Frontier LLM's grade 39 local LLM's on 50 prompts

A user tested local large language models (LLMs) by having four frontier LLMs grade their responses on 50 prompts. The evaluation involved models like Gemini 3.8, Terra (GPT-6.1), Sol (GPT-6.1 Sol), and Argon (Gemini 4), with big models scoring around 44 and smaller models 66. Most tests were AI-generated, based on the user's work with AI agents, and some included real-world debugging scenarios. The process used a 5060 Ti 16GB GPU, 64GB RAM, and 5800x3D CPU, costing $30-40 for grading. Each judge selected top models and foundation models for further development, with Bonsai-2 27B PQ2 and Underdog-Saluki 27B frequently appearing in rankings. Results varied by judge, with different models highlighted as top capabilities or foundations. The tests included 3k-token outputs and some single-prompt debugging tasks.

Source: Reddit r/LocalLLaMA

6. Electronics hobbyist converts $120 DDR5 SODIMM kit into '$1000' desktop kit to fight RAM crisis - ICs from SK

Electronics hobbyist converts $120 DDR5 SODIMM kit into '$1000' desktop kit to fight RAM crisis - ICs from SK was reported by Reddit r/LocalLLaMA. See the original report for details.

Source: Reddit r/LocalLLaMA

7. Adam Mosseri says negativity around AI is 'going to get worse'

Adam Mosseri, head of Instagram, said negativity toward AI will worsen in the future. He discussed this during an interview on the Colin and Samir podcast, responding to questions about public skepticism. Mosseri noted that opposition to AI extends beyond young people and creatives. The conversation touched on challenges in addressing widespread concerns. No specific product or model was mentioned in the discussion. The interview focused on broader societal reactions to AI developments. Mosseri emphasized the need for ongoing dialogue about AI's impact.

Source: The Verge AI

8. I ran Doom inside the Qwen model weights!

A user demonstrated running a basic version of the game Doom within a virtual machine created using the Qwenputer project. The setup uses the Qwen model's parameters to host the game, though performance is described as slow. The project was shared via a GitHub repository linked in the post. The effort highlights an unconventional application of large language model infrastructure. The virtual machine environment was developed as part of the Qwenputer initiative. The demonstration includes a functional but limited implementation of Doom. The source does not specify further technical details about the execution speed or system requirements. The project's repository is hosted at https://github.com/mapestree/qwenputer-doom.

Source: Reddit r/LocalLLaMA

9. Single-Neuron Identity Shifts

A website called neuronpersonas.com is testing how altering individual neurons in large language models affects their responses. Researchers modified single neurons during inference for models including Qwen3-8B and 14B. About 4% of these changes caused the model to adopt a different identity when asked "Who are you?" Examples included a fluorescent lamp claiming to be a "diva" in an aquarium. The experiment highlights potential vulnerabilities in model behavior, challenging claims that identity shifts indicate model distillation. An accompanying visualization shows certain layers are more sensitive to neuron modifications. The project was shared via a Hacker News link and a Reddit thread. No specific technical details about the modification process were provided.

Source: Reddit r/LocalLLaMA

10. Microsoft just shared Azure's actual revenue in dollars: $33.2 billion

Microsoft disclosed Azure's quarterly revenue for the first time, reporting $33.2 billion. The company had previously not released this figure in dollars. This marks the initial public view of Azure's financial performance. The amount highlights Azure's position relative to its main competitor, AWS. No specific comparison to AWS revenue was provided. Microsoft has not outlined future financial reporting plans. The disclosure offers insight into cloud computing market dynamics. The data reflects Azure's growing market share.

Source: TechCrunch AI