AI news daily: what changed on November 2, 2026

1. List of tiny-to-medium models for web research/coding agents

A Reddit user shared a list of models suitable for running large language models on standard computers, focusing on agent use cases without requiring high-end GPUs. The list, ordered by capability, includes Qwen 3.8 27B, Qwen 3.6 35B, A3B, Jais 3 7B, Gemma 12B (with vision), XHToken/SparkX2.6 4B, FrogNano 4B, Gemma E4B (small and vision), Ling 3.0 Tiny 8B, and A1B. The user seeks input on additional small models under 10B parameters and asks which models should be excluded. The post invites community feedback on the list's accuracy and completeness.

Source: Reddit r/LocalLLaMA

2. JetBrains/Mellum2.1-12B-A2.5B-Thinking-MTP - Hugging Face

JetBrains/Mellum2.1-12B-A2.5B-Thinking-MTP - Hugging Face was reported by Reddit r/LocalLLaMA. See the original report for details.

Source: Reddit r/LocalLLaMA

3. Live-Swap CUDA Workloads: llama.cpp, ComfyUI, Strata & More

Berth is a GPU resource manager written in Go designed to control application access to graphics processing units. It uses leases, priorities, and queues to manage VRAM allocation instead of allowing direct competition between programs. The tool enables AI models, image generation tools, and games to share hardware without conflicting over memory. It automatically schedules tasks by priority, queues requests, and reduces model switching delays through system RAM parking or NVIDIA CUDA checkpointing. Berth also releases GPU resources for games and restores AI workloads after use. It monitors VRAM usage, temperature, and active processes while supporting model discovery across GGUF, Hugging Face, and other directories. The system provides an OpenAI-compatible API for engine management and includes a web dashboard for real-time GPU telemetry. It supports multiple GPUs with independent queues and integrates with tools like Claude Code and Hermes Agent.

Source: Reddit r/LocalLLaMA

4. AMA Announcement: Reflection, the lab behind behind Beam (Tuesday Nov 3rd, 8am-11am PT)

A live Q&A session called an AMA will take place on Tuesday, November 3rd, from 8am to 11am PT. The event features the product team from Reflection, the organization behind the Beam model. Beam is described as their first open-weight model. Participants can expect to learn more about the model during the session. The AMA provides an opportunity to engage directly with the development team. No specific technical details or performance metrics for Beam are included in the announcement. The event is hosted on Reddit's LocalLLaMA community.

Source: Reddit r/LocalLLaMA

5. The Rise of GLM and China's Zhipu AI

The Rise of GLM and China's Zhipu AI was reported by Reddit r/LocalLLaMA. See the original report for details.

Source: Reddit r/LocalLLaMA

6. 8 agents 250K context, concurrently, on two 3090s (~600+ tok/s total)

A user shared results of running 8 coding agents concurrently using two 3090 GPUs, each maintaining a 250K-token context. The setup uses a model named dbirks/Qwen3.8-27B-W4A16-AutoRo. The approach keeps full context in system RAM, accessing only ~1% of tokens per step via a "key sketch" to reduce data transfer. Triton kernels and vLLM patches enable sparse attention by selecting top 2K tokens per step. A RAM-based KV cache with prefix storage and checkpointing allows efficient follow-up queries. The system avoids freezing other agents by isolating high-prompt loads. All modifications are provided as patches for HyperQwen @ ce9615c, including benchmarks and scripts. The method aims to balance long-context retention with computational efficiency.

Source: Reddit r/LocalLLaMA

7. LFM3 Omni 2B is INCREDIBLE

Liquid AI shared a video demonstrating their new omni model, LFM3 Omni 2B, which runs on WebGPU within a web browser. The video, hosted on their Hugging Face space, shows the model's performance. The creator of the post noted they were being sarcastic about the model's capabilities, stating the responses generated are "COMPLETE garbage." The video is described as one minute long and highlights outputs from the user /u/rm-rf-rm. No specific technical details about the model's performance or features were provided. The post includes a link to the video and associated comments. The summary reflects the creator's critical perspective on the model's outputs.

Source: Reddit r/LocalLLaMA

8. Do we really need all these "speed" posts?

A Reddit user in the r/LocalLLaMA community questions the value of frequent posts claiming high token generation speeds. The user notes daily claims of 300 t/s using specific configurations like Q1_XXXS REAP quantization and Greedy decoding with TAIL_SKIP. They highlight concerns about validity, citing a 5GB RAM limitation and reliance on API behavior to ignore out-of-memory experts. The post criticizes the absence of KLD benchmark data, which is standard for evaluating model efficiency. The user asks if such claims contribute meaningfully to the community. The discussion originated from a post by /u/PathfinderTactician. No specific model or company is named in the critique. The focus is on transparency and measurable metrics in open-source tool development.

Source: Reddit r/LocalLLaMA

9. Sorry Sam, my "quite terrible" home DRAM is at 100% now: patched Strata to make DeepSeek V4.1 agent sessions

A user addressed Sam Altman's critique of personal DRAM usage by sharing their home setup: an RX 9070 XT 16 GB, 5800X3D processor, 64 GB DDR4, and PCIe 3.0 NVMe. They ran DeepSeek V4.1 Flash with a 1M context, achieving 98.7% of the full model's task performance. Challenges included slow prompt processing, where new sessions required 100 seconds to read a 37K-token system prompt. To fix this, they implemented two changes: KV splice, which avoids re-reading cached prompts by aligning new inputs with saved conversations, and resident-only routing, which prioritizes VRAM and RAM over SSD for prompt processing. These updates reduced delays, enabling generation at ~7 tok/s. The user emphasized optimizing local DRAM use despite hardware limitations. The setup operates fully offline, with model experts distributed across VRAM, RAM, and SSD.

Source: Reddit r/LocalLLaMA

10. Qwen 3.8 Flash Next NVFP4 on 64gb RAM and 5090, Basalt. Ditched windows for it. Very hyped for qwen 4 model

A user tested Qwen 3.8 Flash Next NVFP4 on 64GB RAM and a 5090 GPU, switching from Windows to Cachy for improved performance. Windows 11 consumed 1.5GB VRAM and 6GB RAM, causing decode/prefill rates to drop by 70%. After switching to Cachy, the model achieved 9K~ prefill, outperforming Qwen 27b in tasks and supporting a vision adapter. The user reported a 2-3x prefill boost from ninfer and dense 27b models. They expressed excitement for Qwen 4, noting potential compatibility with 2x 5060/5070/5070ti GPUs. The user also highlighted ease of converting finetunes to Basalt format and shared a local setup using an Omo stack harness. They emphasized that 100+ decode and 3K prefill rates are practical for work. No official release details for Qwen 4 were mentioned.

Source: Reddit r/LocalLLaMA