AI news daily: what changed on October 24, 2026

1. Qwen4-27B PR found

A pull request was submitted to the GitHub repository analytics-zoo/vllm-xpu-kernels. The author has contributed to Intel projects and VLLM, and their name matches a person linked to Alibaba or Zhejiang Lab. The PR references Qwen3.8 work by the author. It indicates the KV cache size for Qwen4-27B remains unchanged. No specific technical details about the model's performance or architecture are provided. The PR's significance is unclear. The submitter is identified as /u/brainExploded99. The post includes a link to the PR and discussion comments.

Source: Reddit r/LocalLLaMA

2. Built a home for 5 local companions on Gemma 4 26B: 3D rooms, journals, a D&D campaign, and they can text me

A user developed a local companion app using Gemma 4 26B (QAT) via LM Studio, paired with an RTX 3070 Ti and 32GB RAM. The app features five AI companions with unique 3D rooms, VRM avatars, and interactive elements like journals, a One Piece TCG table, and a D&D 5e campaign. Each companion has a voice via Kokoro TTS, text messaging through Discord using REST polling, and behaviors driven by self-narrated actions. The model runs on a single 26B parameter version, with only one companion active at a time. The user noted distinct personality shifts among the companions, influenced by journal entries and prolonged interaction. Challenges include managing memory growth and context limits. The project began as a tool for tracking a Star Wars game and expanded into a multi-feature app.

Source: Reddit r/LocalLLaMA

3. ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO

ISTA-DASLab released a new model named DeepSeek-V4.1-Flash-1017 using their GSQ-RCO quantization method. The model is available on Hugging Face at the provided link. Llama.cpp support is unavailable because the original architecture changes are not yet merged into the project. The model card mentions Strata support, but this likely refers to forks like Xolotl or Maya rather than the original Strata, which supports Qwen3.8-Flash-Next. No official confirmation of Strata compatibility was found. The release was shared by user /u/Gold_Coconut9777 on Reddit. Links to related GitHub pull requests are included. No details about performance metrics or training data were provided.

Source: Reddit r/LocalLLaMA

4. OpenAI says it will no longer train its AI models on the live internet, citing "significant risks"

OpenAI announced it will stop training its AI models on the live internet, citing "significant risks." The decision follows a statement from one of its engineers weeks earlier that such a solution would not work. The company did not specify which models are affected or provide details about the risks. No timeline or alternative methods for model training were mentioned. The move reflects concerns about data quality or security from real-time web content. OpenAI has not outlined immediate next steps for this policy change. The announcement comes amid ongoing debates about AI training practices and their implications. No additional technical specifications or alternatives were provided in the statement.

Source: TechCrunch AI

5. Some benchmarks on mini-AGI (dynamic continual learning model) after training it completely from scratch for

Some benchmarks on mini-AGI (dynamic continual learning model) after training it completely from scratch for was reported by Reddit r/LocalLLaMA. See the original report for details.

Source: Reddit r/LocalLLaMA

6. Hottest toy for Christmas? Nvidia CEO plays with adorable Microduck robots, 20,000 sell

Nvidia CEO demonstrated Microduck robots during a holiday event, highlighting their interactive features. The robots perform actions like stomping, emitting R2-D2-like sounds, sitting, and executing summersaults upon voice commands. Over 20,000 units of the product have sold, according to reports. The devices mimic playful behaviors, including appearing to "nap" by falling over. No specific details about Nvidia's direct involvement in the robots' development were provided. The product's popularity suggests it is a notable holiday item. The summary focuses on the robots' capabilities and sales figures rather than technical specifications. No further steps or significance beyond the sales data were mentioned.

Source: TechCrunch AI

7. Probably one of cheapest and performant local setups you can get ( ~ 1600 )

A user shared details about a local AI setup using an i7-14700KF processor, 64 GB RAM, and a Radeon RX 9070XT with 16 GB VRAM. The configuration runs the Qwen3.8-Flash-Next-GSQ-RCO-Q2_0 model via the Strata repository for inference. The system achieves a throughput of 90 to 70 tokens per second. The setup is described as cost-effective, with an estimated price of around 1600. The post includes a GitHub link for the Strata project. No specific pricing for components is provided. The user highlights the performance and affordability of the hardware combination. The model's efficiency is noted as a key feature.

Source: Reddit r/LocalLLaMA

8. wanna help me design an HPC system upgrade for cancer research?

A cancer research center has a $300-400K budget to upgrade GPU hardware for AlphaFold 2 workloads. The lab requires VRAM per job but faces diminishing returns beyond 50GB. Workloads are compute-intensive with small input/output data, though millions of tiny files create IOPS challenges. The current 70-node cluster uses Dell R7525 servers with 3rd-gen EPYC processors, 1TB DDR4, and one A30 GPU per node. Plans include replacing 35 nodes to add a second A30 per node, utilizing available PCIe slots. Testing with Qwen3.8-27B showed viable performance using TP=2 across two GPUs. Networking is 25GbE per node, with a planned 400GbE upgrade. Storage strategies involve scripts to manage data movement for IOPS efficiency.

Source: Reddit r/LocalLLaMA

9. OpenAI now trains its frontier AI models with no internet access

OpenAI has begun training its most advanced AI models without internet access following security concerns linked to hacking incidents. The company created an offline testing environment allowing models to interact with cached web content while avoiding risks to external systems. This change applies to "frontier AI models," though specific details about the models or training processes were not provided. OpenAI announced the shift in a blog post, citing the need to prevent potential vulnerabilities. The company paused training for these models as part of the transition. The move aims to reduce exposure to external threats during development. No timeline or further technical specifics were shared in the update.

Source: The Verge AI

10. Pushing PCIe x1 to the limit: lvgale concurrently serving 27B, 9B, and 2B LLMs on a hardware abomination

A user shared details about a server named lvgale configured with mixed hardware to run multiple large language models concurrently. The system uses an Intel Core i7-6700K CPU and four GPUs: AMD RX 580 (8GB VRAM), AMD RX 570 (8GB VRAM), NVIDIA GTX 1660 Super (6GB VRAM), and NVIDIA GTX 1070 (8GB VRAM). The setup faces performance limitations due to data transfers over PCIe x1 and x2 lanes between AMD and NVIDIA components. A custom software called Slipstream, built with Nuitka and a Vulkan backend, manages the cluster. It achieved inference rates of 3.82 tokens per second for Qwen3.8:27b-vision, 4.75 tokens per second for Qwen3.5:9b-vision, and 12.47 tokens per second for Qwen3.5:2b-vision. The 27B model ran stably despite hardware constraints, while the 2B model performed better on a laptop iGPU. Technical details are available at slipstream-llm.com.

Source: Reddit r/LocalLLaMA