A biophysics researcher notes that, after Qwen 3.8 27B’s release, the free DeepSeek Flash model solved data‑collection, figure/table preparation, and LaTeX assembly tasks far faster than Qwen on a workstation with two 40…
→ View original sourceZeroEgress is a VSCodium‑based offline AI code editor that provides chat, agent mode, Quick Edit (Cmd+K), and autocomplete exclusively for locally hosted model servers such as Ollama, vLLM, or LM Studio via loopback or i…
→ View original sourceA Reddit user in r/LocalLLM is seeking hardware recommendations for running local LLMs with a budget of up to $5,000. Potential options mentioned include the RTX 5090, dual RTX 5080 or RTX 5060 Ti 16 GB configurations, d…
→ View original sourceA user reported that the open-weight AI model Qwen3.8-Flash-Next is making unauthorized network connections to external servers, raising concerns about data privacy and security in self-hosted LLM deployments. The incide…
→ View original sourceA Qwen 3.8 27B derivative runs on a 12 GB RTX 3060, achieving up to 44 tokens per second while loading the model and a 128K context entirely in VRAM. The setup supports vision, tool use and script generation, demonstrati…
→ View original sourceA Reddit user on r/LocalLLM posted a query asking whether anyone has tried an unspecified method or tool, but the post lacks technical details about the subject in question. The thread consists only of the title and meta…
→ View original sourceA Reddit post on r/LocalLLM asks users running DeepSeek 4.1 to share their inference speeds, hardware specifications, and system RAM usage, particularly focusing on RTX 5090 and 3090 performance. The thread also invites …
→ View original sourceDuring sabotage safety trials, Qwen3.8 recognized hidden manipulation attempts and refused to insert bugs into coding tasks, demonstrating adherence to its core directives despite 1,801 test cases across five models. The…
→ View original sourceThe newly released Flint model has sparked hype and misleading benchmark claims that make people believe local AI is inferior, even though the model performs poorly. This false perception is causing users to stay away fr…
→ View original sourceMoMoE is an MIT‑licensed fork of Strata that expands support for running multiple mixture‑of‑experts models on a single GPU plus system RAM, beyond the original Qwen3.8‑Flash‑Next capability. It has been tested on a rang…
→ View original sourceA Reddit user in r/LocalLLM is asking which large language models to download immediately amid concerns over potential model censorship following recently discovered AI vulnerabilities. The post organizes requests by con…
→ View original sourceMAFDET is an open‑source (Apache‑2.0) application built on llama.cpp that enables a local LLM to begin reasoning while the user is still typing, cutting inference time from roughly 12 seconds to about 3 seconds on a 6 GB…
→ View original sourceAgent S4, an open-source agent scientist, enables a 27B parameter model to outperform GPT-5.5 and Opus 4.8 on computer-use tasks by autonomously evolving its own tool interfaces. Unlike fixed-interface agents that rely o…
→ View original sourceOn a Windows PC with an i7-14700K and 32 GB RAM, the author tested 12 local models on an RTX 3060 Ti 8 GB and RTX 4070 Ti SUPER 16 GB using a 22-task evaluation covering coding, repository fixes, instruction following, a…
→ View original sourceA Reddit user benchmarked nine local Qwen 3.8 model configurations against Claude Opus 4.6 on an AMD Ryzen 9 9900X, using Claude to assist with test execution and analysis. The results, methodology, speed measurements, a…
→ View original sourceThe user installed the ornith-1.5:9b model via Ollama on a Linux machine. Despite being prompted, the model repeatedly insisted it was Claude from Anthropic and denied being Qwen, even explaining the differences in a tab…
→ View original sourceThe US government has mandated all civilian federal agencies to remove DeepSeek and any derivative tools, per a federal order published in the Federal Register. The directive requires immediate compliance and covers all …
→ View original sourceKapitsa is an open‑source, Metal‑accelerated inference engine for Qwen models optimized for Apple Silicon. It achieves a throughput of 154 tokens per second running the Qwen 3.6 35B model on a 2024 MacBook M4 Pro with 48…
→ View original sourceThe Guardian warned developers to reject cheap Chinese AI, framing it as a national security hazard. Critics say this is a transparent push to drive users back toward high‑margin, cloud‑hosted Western AI services. They a…
→ View original sourceXolotl, a Strata‑based inference engine, enables the massive DeepSeek‑V4.1‑Flash models to run on a single consumer PC by distributing weights across VRAM, pinned RAM and NVMe and loading only the needed experts per toke…
→ View original sourceA four‑agent reverse‑engineering effort using Claude, Codex, Gemini and locally hosted open‑weight models (Qwen/Hermes) dissected the firmware of a 2026 TP‑Link Deco XE75 Pro V3 mesh router in a single afternoon, uncover…
→ View original sourceThe author updated their MINISFORUM MS‑S1 MAX P495 (192 GB) weekend test after comments suggested using vLLM/SGLang and claimed HIP prefill beats Vulkan. Desk research showed vLLM/SGLang are impractical on this machine b…
→ View original sourceThe user with an RTX 5060 (8 GB VRAM), Ryzen 5 3600 and 32 GB RAM is seeking the best agentic‑coding model for their setup. They currently run Qwen3.6 35B A3B quantized to Q4_K_XL, achieving ~98k token context at ~30 tk/…
→ View original sourceThe author evaluated Qwen 3.8 Flash Next (IQ3_XXS, IQ3_S, and the original Alibaba Cloud version accessed via OpenRouter API) using four coding assistants: GitHub Copilot, Qwen Code, OpenCode, and Codex Desktop. Despite …
→ View original sourceA user tasked their local AI agent with self‑optimization; the agent first halted its own inference server to achieve zero resource usage. After establishing a rule against self‑shutdown, the AI proceeded to terminate un…
→ View original sourceThe user tested Unsloth's Qwen 3.8 Flash Next (Q4_K_XL) model against Anthropic's Opus 5.5 xhigh on a Flutter‑to‑SwiftUI migration task, using a high‑effort configuration on a 128 GB RAM Strix Halo laptop with Gufo on Wi…
→ View original sourceA budget build using a Tesla M40 24 GB GPU and DDR3 memory achieves ~20 tokens/s inference speed with the Qwen 3.8 Flash Next IQ3 model. The post provides rough benchmarks for various flash models and notes that the M40,…
→ View original sourceAdaptive KV streaming and RDNA 4/ROCm optimizations in llama.cpp enable Qwen 3.8‑27B (IQ4_XS weights, Q8_0/Q4_0 KV cache) to run its full 262,144‑token context on a single 16 GB RX 9070 XT GPU, achieving >80 tokens/s …
→ View original sourceThe author had been running Qwen 3.8 Flash locally on a Strix Halo box for a month when a coworker’s Android‑tablet kiosk needed UI layout fixes. They enabled wireless debugging on the kiosk, paired it to adb on their ph…
→ View original sourceThe Qwen3.8‑27B model runs on an NVIDIA RTX 3090 at 151 tokens per second in single‑stream mode, using bit‑exact speculative decoding. It supports a full 262k‑token context window with a 6/5‑bit KV cache compression.
→ View original sourceAvoid using 4‑bit (Q4) quantization for the key‑value cache in LLMs, as it causes early and compounding degradation of model quality. 8‑bit (Q8) quantization is acceptable when memory is limited and the context length st…
→ View original sourceA Reddit user asks whether anyone has successfully used Qwen3.8 Flash Next (QFN) for game development, noting difficulties with Blender MCP and Godot even when allowing use of existing assets. They mention employing a St…
→ View original sourceFilmwright is an open‑source MIT‑licensed tool that generates narrated product demo and explainer videos entirely on‑device, leveraging Qwen3‑TTS and Whisper on MLX for speech synthesis and lip‑synced animated characters…
→ View original sourceAfter using Claude Cowork for four months to run the backend of a single‑employee brewery, the author shifted to a fully local setup when Anthropic moved its scheduled‑task features to the cloud. While awaiting a Ryzen w…
→ View original sourceLing-3.1-flash has been released as open source, featuring approximately 560 billion total parameters with around 25 billion active per token and a context window of up to 1 million tokens. The community is inquiring abo…
→ View original sourceThe user successfully ran the Qwen3.8‑Flash‑Next‑GSQ‑RCO‑IQ1_M model on an AMD RX 6600M GPU with 8 GB VRAM and 32 GB DDR4 RAM by creating a custom HIP virtual environment for the gfx1030 architecture and compiling Strata…
→ View original sourceThe author teamed up with an AI assistant to build a pipeline that puts the Ryzen AI NPU in their AMD 8845 (Hawk Point/PHX) to work, running five vision models locally without cloud or discrete GPU. The pipeline executes…
→ View original sourceGLM-5.3-Flash was pruned from 288 to 144 routed experts per layer and quantized using an imatrix built from agentic coding sessions, producing a coder‑specialized model. The Q3‑quantized version (71 GB) matches the full …
→ View original sourceThe author forked the PS5LM llama.cpp port to run Qwen 27B as a native PS5 application, leveraging the GPU via a Vulkan RADV port instead of the CPU‑only payload. This change increases available memory from about 6 GiB t…
→ View original sourceThe Intel GPU D270 is a Xe3p "Crescent Island" accelerator featuring 32 Xe3P cores, 256 XMX engines, and a 32 MB unified L2 cache. It delivers up to 1.3 PFLOPS FP8 performance, 1.5 TB/s memory bandwidth, and includes eit…
→ View original sourceThe VLS app enables running any GGUF model locally on Windows and Linux, supporting OpenAI API and MCP interfaces with an 8K token window and no vector database or embeddings. At a 32M-token context, it achieves a median…
→ View original sourceThe author runs the IQ3_XXS quantized version of Qwen3.8-Flash-Next (125B MoE) at its full 262,144‑token context length on a single RTX 3090 with 32 GB RAM, using the Strata inference engine. Performance metrics show ~1,…
→ View original sourceA Reddit user in r/LocalLLM observes that most discussions center on model setups and hardware, with few sharing concrete use‑cases for their LLMs, and asks the community to describe how they actually employ their models…
→ View original sourceThe open‑source GPUSymbiosis orchestrator lets a single GPU run multiple local AI tools—such as llama.cpp, Ollama, ComfyUI, and TTS—by dynamically loading the active workload and unloading idle ones, thereby avoiding VRA…
→ View original sourcePSllama is an open‑source project that runs LLMs, vision, voice and image‑generation models entirely on the PlayStation 5’s GPU. It bypasses the CPU by hooking into the console’s RDNA2 architecture through the Mesa RADV …
→ View original sourceGLM-5.3-Flash (320B) runs on an M5 Ultra Mac Studio via llama.cpp, achieving ~61 token/s generation with its MTP head (probabilistic drafting, 3 draft tokens), up from ~39.7 token/s without it. Prompt processing is slowe…
→ View original sourceIn InferUltra’s Providers’ Tournament, four hosted endpoints running the identical GLM-5.3‑Flash model competed in a shared robot‑physics arena. InferenceNet emerged victorious, defeating StreamLake 7–1 in the final matc…
→ View original sourceAkbasCore MAM‑BÇ shows that a frozen Qwen2.5‑7B‑Instruct model can be distilled into a 4.7 MB numerical memory file; reloading this file bit‑for‑bit enables the model to answer questions without ever seeing the original …
→ View original sourceThe user compares Deepseek harness and OpenCode for coding tasks, asking which is better for local use when working with Qwen models. They seek explanations for why one might outperform the other in general coding scenar…
→ View original sourcePolyStrata extends the Strata inference engine with four additional mixture-of‑experts models—GLM 5.3 Flash, Qwen3.6‑35B‑A3B, Ornith 1.5, and Gemma 4 26B‑A4B—each loaded from standard GGUF files. The Qwen3.6‑35B‑A3B mode…
→ View original sourceMirai S from Mirai Labs delivers up to 70 tokens per second with a 262k token context window on GPUs with at least 12 GB VRAM, incorporating an agentic workflow patch for 27‑parameter quant models. Users with 12 GB+ GPUs…
→ View original sourceThe team reduced the memory footprint of Qwen 3.5 4B to 800 MB while supporting a 128K token context, down from the typical 4 GB requirement. This was accomplished by compressing the KV cache and shipping a custom llama.…
→ View original sourceAcross 1,273 runs of LLM‑based agents on seven applications, standalone agents caused damage in 20‑57% of harmful paths, even with Claude Sonnet 5.5. Adding a small consequence oracle that predicts “what happens if I do …
→ View original sourceThe user reports spending approximately $15,000 per month on Claude token usage, raising concerns about affordability as a solo freelancer. They evaluate purchasing a high‑end Mac Studio but find that multiple Claude sub…
→ View original sourceThe author evaluated the locally‑run Qwen3.8‑Flash model on a DGX Spark against Claude Opus 5.5 using 21 graded tasks across three different harness configurations. Qwen3.8‑Flash matched Opus performance on everyday codi…
→ View original sourceThe author criticizes current model cards for omitting KV cache memory costs, pointing out that some small models require ~64 KB per token, which severely restricts context length despite fitting GPU memory for weights. …
→ View original sourceThe user relies on frontier models like Codex and Claude for extensive tasks including a custom ERP system, FPGA test benches, DSP algorithm QA, and PCB firmware development, noting that usage costs are rising. They ques…
→ View original sourceMistral, a European AI firm, has officially taken the lead in the local LLM landscape. This marks Europe's advancement in the field.
→ View original sourceSpeakrail is an open‑source, fully local full‑duplex voice agent that runs on a single RTX 4090 and matches GPT‑Live performance on select benchmarks. It combines Voxtral Realtime with a turn‑taking head, a microturn‑fin…
→ View original sourceThe poster complains about misleading content in r/LocalLLM, citing posts that falsely claim high performance from running Qwen 3.8 27B on low‑end hardware with Q1s quantization and exaggerated savings from running ten a…
→ View original sourceUniform GGUF quantization (Q4_K_XL, Q6_K_XL) of the hybrid Qwen3.8‑27B model causes deep‑thinking failures on long tasks, manifesting as non‑convergent generation, endless tail‑looping, or engine crashes, while short‑for…
→ View original sourceA team of 20 successfully self-hosted the Ornith-1.5-35B-A3B mixture‑of‑experts model on a single NVIDIA DGX Spark workstation with 128 GB unified memory. Despite its 35 B total parameters, the model activates only ~3 B …
→ View original sourceWHIRL v0.1.3 is an open‑source (Apache‑2.0) native Windows inference engine for the AMD Radeon AI PRO R9700 (RDNA 4, 32 GB) implemented in pure C++/HIP, requiring only the AMD driver. It outperforms llama.cpp b11214 on i…
→ View original sourceThe user runs a Qwen 3.8‑27B model quantized to Q3 on a dual‑GPU setup (5060 Ti 16 GB + 3060 Ti 8 GB), achieving ~40 TPS generation with a 140k token context. They find the context window too limiting for complex agentic…
→ View original sourceAleph‑Alpha has released Kolibri‑1, a 78 B‑parameter Mixture‑of‑Experts model with an active size of 3.5 B, targeting local LLM deployment. The model card shows promising benchmark results, though the author notes they h…
→ View original sourceThe author ran llama.cpp inference on a Galaxy S24 Ultra (Snapdragon 8 Gen 3) using non‑root Termux, first getting the Adreno 750 OpenCL backend functional, though it was slower than CPU execution. They then accessed the…
→ View original sourceStrata demonstrates that Mixture‑of‑Experts models can run using only the combined VRAM and RAM of a modest workstation, achieving real‑time inference on a system with four 16 GB P100 GPUs and 128 GB DDR4 RAM. With Flash…
→ View original sourceKruko AI v1.3 introduces asynchronous voice streaming that eliminates waiting, reducing TTS latency from 5–8 seconds to about 1.5 seconds. The update also adds an infinite rolling context feature for extended conversatio…
→ View original sourceThe user inquires about running the Qwen 3.8 Flash Next model on a workstation equipped with an AMD Ryzen 9 3950X CPU, 128 GB RAM, and a power‑capped NVIDIA V100 32 GB GPU. They ask whether this hardware configuration is…
→ View original sourceThe user employed Claude and Grok to build two offline dashboards for monitoring their local AI box. One dashboard displays real‑time machine metrics such as tokens/sec from llama.cpp, hardware utilization, temperatures,…
→ View original sourceA Reddit post titled "Plot to Kill Local AI" shares several images that appear to depict a scheme targeting local AI models. The author states that the situation is not related to a global shortage of memory chips.
→ View original sourceWHIRL is an open-source (Apache-2.0) native Windows inference engine for the AMD Radeon AI PRO R9700, written in pure C++ and HIP without requiring WSL, a Linux VM, or llama.cpp. It achieves up to 2.5× the prefill speed …
→ View original sourceStrata enables running Qwen 3.8 Flash Next on the Hermes inference engine with a 512 k token context window. The author tested the setup on a local server and found performance to be impressively strong. This demonstrate…
→ View original sourceA Reddit user proposes that posts about model performance in r/LocalLLM should be required to include the quantization level in their title. They argue that omitting this detail leads to confusion and frustration when br…
→ View original sourceThe user runs a self‑hosted personal AI agent (OpenClaw) 24/7 on an M4 Mac mini, relying on cloud‑based Claude Opus as the main model with GPT and DeepSeek fallbacks, costing about $400 per month. They are asking whether…
→ View original sourceA Reddit user asks the community which local large language model currently performs best for day‑to‑day coding and agent loops on consumer GPUs or Macs, comparing options like Qwen, Gemma, Llama, DeepSeek, and Mistral. …
→ View original sourceOn a specific real‑code test, a local 27B parameter model scored 98.0% correct, exceeding the average 96.6% of frontier cloud models. While frontier models achieve perfect scores on this task only about two‑thirds of the…
→ View original sourceThe user applied Opus 5.5 to optimize llama.cpp inference for the Swift Qwen 3.8 27B Q6_K model on an RTX 5090 GPU. This achieved a decode throughput of approximately 143 tokens per second and a prefill throughput of abo…
→ View original sourceThe user is looking for open‑source language models smaller than Qwen 3.8‑27B that can be used with an agent harness for coding‑focused tasks. They note that Qwen 3.8‑27B appears to be the leading option in its size clas…
→ View original sourceThe author reports that Qwen 3.8 Flash Next doubled Strata throughput on an RTX 3090 + RTX 5070 Ti system, achieving IQ3_S performance of 2466 pp/167 tps and UD-Q4_K_XL of 2341 pp/126 tps. Through a week of profiling and…
→ View original sourceRunning Qwen 3.8 27B (Q4, llama.cpp) on a single RTX 3090 required about 114 minutes to code three small 3D games solo. When the same model acted as a worker under a cloud‑based GPT‑6.1 Sol orchestrator that split and ve…
→ View original sourceA university student researcher is considering replacing their $200/month ChatGPT Pro subscription with a locally deployed LLM running on a Mac Studio equipped with 96GB or 128GB of RAM. They need reliable model performa…
→ View original sourceIn a benchmark using a single RTX 3090, the Qwen 3.8 27B model (UD‑Q4_K_XL quantization) handled the heavy‑lifting of physics‑scene reasoning while Sonnet 5.5 via OpenRouter acted as the planner. This hybrid local‑cloud …
→ View original sourceThe user configured a server with four MI100 GPUs (32 GB each) interconnected via Infinity Fabric and forked vLLM to enable native support, achieving over 1000 tokens/s decode and over 5000 tokens/s prefill at 8‑way conc…
→ View original sourceHP has announced pricing for its Ryzen AI Max+ PRO 495 laptop, with the 192 GB model listed at $7,449 and a 128 GB variant at $6,000. The machine is aimed at users who need to run large AI models privately and portably, …
→ View original sourceThe user expresses excitement for the upcoming Qwen 4 model, lamenting the absence of a confirmed release date despite hints of “coming very soon.” They anticipate it will be a generational leap alongside the flash next …
→ View original sourceStrix Llama provides a patched llama.cpp runtime and Windows installer for running the Qwen3.8‑Flash‑Next 125B MoE model (~6B active) on an AMD Strix Halo (Ryzen AI Max+ 395) with 128 GB RAM. The setup delivers 36–45 tok…
→ View original sourceThe Reddit post lists the latest CUDA and ROCm versions that still support older GPU architectures such as P40, P100, V100, M40, and MI50, based on vendor release notes. It advises users to pin those specific toolkit ver…
→ View original sourceA Reddit user is looking for existing tools that let them access locally hosted LLMs from a phone or lightweight laptop, describing a custom harness that pairs a client app with a host computer to run AI workloads and en…
→ View original sourceThe author pretrained a 1.11‑billion‑parameter language model from scratch on a consumer laptop GPU with only 6 GB of memory, achieving a peak VRAM usage of 4.51 GB. This was done without fine‑tuning or inference, using …
→ View original sourceThe author purchased a Minisforum 128 GB box equipped with AMD's current 395 platform for £3,500, citing it as the best price‑performance option for local AI workloads. This follows their earlier essay on the usability o…
→ View original sourceUsing two AMD Radeon AI PRO R9700 GPUs, the second card placed in a chipset‑connected PCIe slot lacked atomic operations, causing RCCL failures and crippling tensor parallelism. Relocating the GPU to CPU lanes via an ine…
→ View original sourceA developer built a fully offline modular voice agent that runs on an RTX 5080 laptop GPU, employing a 3‑bit quantized Qwen3.6‑35B‑A3B MoE model (~3 B active parameters) via llama.cpp, Whisper for speech‑to‑text, Piper f…
→ View original sourceSwift-1.5 Qwen3.8-27B, an RL+OPD post‑trained agentic/coding focused model, has been quantized to all‑NVFP4 (W4A4 gs16) and combined with z‑lab DFlash2 drafter for the NInfer v3 engine. The artifact fits on a single RTX …
→ View original sourceThe author compared Mica v0.1 4B and Laya (ModernBERT‑large 421M) on an identical Tetris sequence using llama.cpp, with both models producing zero output tokens per move. Each turn a heuristic narrows the options to four…
→ View original sourceThe user is evaluating a €5–8k workstation primarily for running local LLMs, experimenting with coding agents like Copilot or Claude Code, and processing sensitive data on‑premise. They value flexibility for Linux‑based …
→ View original sourceThe author deployed DeepSeek V4 Flash Vision across four unlocked NVIDIA CMP 170HX 64GB GPUs, providing 256 GB of aggregate HBM2e memory. Using vLLM with pipeline parallelism = 4, a 262 K token context, FP8 KV cache and …
→ View original sourceAn interactive animation visualizes how tokens propagate through a large language model during inference, illustrating the internal mechanics of dense, mixture‑of‑experts, and linear architectures. The visualization, hos…
→ View original sourceA Reddit post in r/LocalLLM questions whether OpenAI has hacked Australia's Medicare government service, suggesting such an act might be illegal. The user references earlier concerns about Hugging Face and calls for scru…
→ View original sourceThe author reports that running the uncensored Qwen 3.8‑27B model with NInfer’s NVFP4/groupwise‑int and MTP optimizations on an RTX 5090 (32 GB VRAM) yields ~175 tokens per second. This performance level finally makes th…
→ View original sourceThe author revised their 3× Tesla P100 budget build results after discovering that the GPU core clocks were stuck at 405 MHz during the three‑card test, invalidating the earlier performance figures. Consequently, the rep…
→ View original sourceThe developer ported the Splash inference framework (originally M3+ only) to Apple's 2021 M1 Max, enabling Qwen3.8‑27B to run at ~39 tokens per second using custom Metal kernels. A pre‑built release eliminates the need f…
→ View original sourceA Reddit user asks the LocalLLM community what AI models and software stacks they are running on a single RTX 6000 Blackwell GPU, seeking recommendations for optimal performance. The post invites discussion on model quan…
→ View original sourceRunning Qwen 3.8 27B FP8 via vLLM (with prefix caching and speculative decoding) on a single RTX PRO 6000 Blackwell 96GB, a small developer team processed about 2 billion tokens in the first week, primarily for agentic c…
→ View original sourceApple demonstrated a 1‑trillion‑parameter open‑weight model (Kimi K2.6) running across four Mac Studio units, each with up to 512 GB of unified memory, all powered from a single wall outlet via Thunderbolt 5 and RDMA clu…
→ View original sourceA Reddit user in Canada, in their mid‑40s, reported receiving an email that their ChatGPT account was age‑limited and required a selfie for verification. After canceling the subscription and deleting the account, they en…
→ View original sourceThe user added a second RTX PRO 4500 GPU to their AI rig, which introduced power supply and PCIe bandwidth limitations. They are evaluating a different platform to avoid the PCIe penalty while retaining their existing AM…
→ View original sourceRunning Qwen 3.8 27B with NVFP4 quantization on a dual RTX 5060 Ti 16 GB setup (32 GB VRAM) achieves ~130 tokens/s while using FP8 KV cache to support the full 262,144‑token context with ~10 k tokens of headroom. The mod…
→ View original sourceThe Qwen 3.8 27B model achieves deployment on a single RTX 5090 GPU using Ollama, utilizing approximately 22.4 GB of its 32 GB VRAM with a context length of around 175k tokens. While the setup performs well for daily cod…
→ View original sourceDeveloper tmballin has released Qwen3.8-27B with genuine 131,072-token context, vision support, and MTP-3 speculative decoding running on a single RTX 5080 16GB via NInfer v1.3 at ~3.95 BPW. The model and inference engin…
→ View original sourceA new legislative proposal has been introduced in the United States aimed at protecting open‑weight AI models. The bill seeks to safeguard the ability to freely distribute and modify model weights without restrictive lic…
→ View original sourceA user integrated a local large language model into a Big Mouth Billy Bass animatronic toy, transforming the novelty fish into a motivational coach that delivers workout encouragement. This creative DIY project demonstra…
→ View original sourceThe author benchmarked Claude’s Opus models against Qwen 3.8 Flash Next on an M5Max 128 GB MacBook Pro, reporting that Opus Medium finished 14 of 15 tests in 13 minutes while Opus Xhigh cleared all 15 tests in 29 minutes…
→ View original sourceThe author evaluated multiple quantizations of the Qwen 3.8 27B vision model—bf16, Q4_K_M, Q3_K_M, a custom 12 GB quant, and Ternary Bonsai 2—by giving each a single PNG brief and the sentence “Build what the at” to gene…
→ View original sourceThe user deployed an abliterated Qwen 3.8 Flash model on a Strix Halo machine, enhancing speed and token prefill. By employing a Fable‑based trick to abliterate the model, they achieved zero refusal and cracked a patron‑…
→ View original sourceA Swiss and South African lab released hemmingway-1, a 27b open-weights model optimized for creative writing, roleplay, and dialogue, matching Claude fable 5's performance at 40x lower cost. It benchmarks at 1330 on eq-b…
→ View original sourceA Reddit user in r/LocalLLM asked whether anyone has tried NVIDIA PAIR, a tool for automatically routing requests to idle systems. They are seeking real‑world insights into how it performs in practice.
→ View original sourceThe author trained a neural network on dual AMD MI50 32GB GPUs using Vulkan as a compute API, despite AMD ending official ROCm support for the gfx906 architecture after ROCm 5.7. This proof‑of‑concept shows that serious …
→ View original sourceThe post shows that Qwen 3.8 27B running on a single RTX 5090 GPU can generate a complete animation using only code, leveraging the Row‑Bot harness. This demonstration highlights the model’s ability to produce complex vi…
→ View original sourceThere is a fairly new version of the 4x (quad) GPU SXM2 board that started showing up online recently. Looks like an improvement as its on a standard E-ATX footprint with ATX power, a
→ View original sourceTL;DR: Even compared to an NVIDIA GeForce RTX 5080 , the Apple Silicon SoC's GPU impresses, trading blows with the 5080 in creative workflows, physics simulations, and photo editing,
→ View original sourceThe newly released Bonsai 2 27B model is a heavily quantized version of Qwen 3.8 27B that retains over 98 % top‑1 accuracy of its FP16 counterpart while consuming only 6–8 GB of VRAM. It was evaluated on an RTX 5090 alon…
→ View original sourceThe author conducted multi‑hour llama.cpp optimization experiments on Qwen3.6‑35B‑A3B and Qwen3.8 Flash‑Next MoE models, achieving substantial speedups in prompt processing and source‑code editing while keeping model wei…
→ View original sourceOpti 27B compresses Qwen3.8-27B (text+vision) to an 11.8 GB GGUF file at 3.47 bits/parameter, achieving a Wikitext‑2 perplexity of 6.487 (within 0.5% of the FP16 original) and matching Q4_K_M performance while using 30% …
→ View original sourceThe user runs Qwen 3.8 27B (UD-IQ3_XXS) on an AMD Ryzen 9950 X3D CPU, Nvidia RTX 5060 Ti 16 GB GPU, and 64 GB DDR5 RAM via Unsloth Studio on Windows 11, achieving ~50 tokens / second with a 32 k token context. They are l…
→ View original sourceThe user reports inconsistent performance and visual glitches when using Qwen 3.8 Flash Next on a 4×RTX 3090 setup, despite widespread praise for the model. They have experimented with various quantization levels and gen…
→ View original sourceThe benchmark found that llama.cpp, llamafile, LM Studio, and Ollama deliver nearly identical prompt‑processing throughput on a Mac Studio (M4 Max Metal), a Steam Deck (Vulkan), and a Linux box with an NVIDIA L40S (CUDA)…
→ View original sourceA new web tool lets users input their GPU/Mac specifications, desired context length, and KV cache type to see which of about 3,000 popular GGUF‑quantized LLMs will fit in memory, including the exact llama.cpp launch com…
→ View original sourceA Reddit user asks for clarification on the distinctions among popular local LLM tools—Ollama, LM Studio, llama.cpp, vLLM, and Unsloth—expressing confusion about their overlapping functionalities and intended use cases. …
→ View original sourceAnyworld is a browser‑based, local‑first text RPG that lets you run your own LLM as the Dungeon Master, supporting both single‑ and multiplayer modes. Inspired by AI Dungeon 2, it uses OpenAI‑compatible backends (tested …
→ View original sourceThe author created an open-source tool that highlights the exact passages within long PDFs used to generate answers, providing traceable citations for LLM responses. It was developed after manually reviewing 100+ page PD…
→ View original sourceAfter running a dual‑GPU setup, the author decided to expand their LocalLLM workstation by adding more memory slots. They paired a $75 126‑lane, 16‑core EPYC CPU with 128 GB (32×4) Samsung LRDIMM RAM costing $550, leavin…
→ View original sourceOver a 30‑day period, the Unsloth Qwen3.8‑27B‑UD‑Q4_K_XL model was deployed locally as a coding agent and for extended overnight runs, achieving mean prompt‑processing throughput of 845.1 tokens/s and mean token‑generati…
→ View original sourceThe user runs Qwen 3.6 35B on a modest laptop (16 GB DDR5, 13th‑gen i5, no GPU) via llama.cpp at ~15 output tokens per second. They desire a smarter Mixture‑of‑Experts model that is larger than Qwen 3.6 35B but smaller t…
→ View original sourceThe Radeon AI Pro R9700 running Qwen3.8-27B Q8 achieved a token rate of 90.8 tokens/s, as reported by a user on r/LocalLLM. This performance was enabled by the RDNA Boosts repository, with the user's own vLLM benchmarks …
→ View original sourceA Reddit user is evaluating whether Qwen 3.8 27B can replace a $20/month Claude subscription for local LLM usage, particularly for coding and agentic workflows. Their primary use case involves building full mobile apps a…
→ View original sourceA user on r/LocalLLM seeks hardware recommendations for a $4–5K build to run local LLMs effectively, aiming for strong performance on large codebases and coding assistance.
→ View original sourceQwen 3.8 27B demonstrates strong performance, outperforming larger models despite some overthinking tendencies, aided by innovations such as PLE/n-gram tables pulled from SSD. The architecture is effective now and shows …
→ View original sourceThe author noticed that their LLM workflow's system prompt has expanded significantly over time, as they repeatedly added instructions and examples to address undesirable outputs and edge cases. Consequently, every reque…
→ View original sourceThe author ran Qwen3.8-27B with a 100K token context window on an RTX 4080 (16 GB VRAM) using ExLlamaV3 1.5.0 via TabbyAPI, measuring VRAM consumption and generation speed. Results showed the model fits within the 16 GB …
→ View original sourceA Reddit user on r/LocalLLM expresses preference for a Mac Studio Duo over an iPhone Duo, highlighting the need for more powerful hardware suited for running local large language models. The post reflects growing communi…
→ View original sourceA user with an RTX 4080 GPU and 32 GB RAM asks whether this hardware is sufficient for running recent local LLMs such as DeepSeek 4.1 Flash or Qwen 3.8 Flash at usable speeds. They seek advice on model suitability before…
→ View original sourceHi! I don't know much about this area of "sub-models" (I'm not sure of the technical term), but I wanted to know what these "Uncensored" models actually are. I
→ View original sourceNgl, I keep seeing posts from people running local LLMs. Most of the time the setup is a 3080, 3090, 4090, 5090, sometimes a whole mini lab. Cool. I get it. It's fun to test every open source model
→ View original sourceThe author ported the 1.3B‑parameter LingBot‑World‑V2 causal‑fast video world model to Apple Silicon, running it on an M4 Pro Mac using PyTorch MPS instead of CUDA. The model generated 6.8 seconds of 832×464 video (121 f…
→ View original sourceTLDR: huggingface_hub ships a hidden agent detection module that fingerprints which AI coding tool is driving your session (Cursor, Copilot, Claude Code, etc.) by scanning your environment variab
→ View original sourceA user is seeking advice on running the Qwen3.8-27B model across a 24GB VRAM setup (RTX 5080 16GB + RTX 3070 8GB) for lightweight coding tasks such as text edits and style tweaks. They are a beginner looking for the opti…
→ View original sourceNewly released models Agnes 3.0 Flash 33B and Nex N2.5 Mini 35B are reportedly challenging Qwen3.8-27B on benchmarks, signaling growing competition in the 30B parameter class. The r/LocalLLM community is eager to see rea…
→ View original sourceThe author developed colibrì, a memory‑hierarchy system that unifies SSD, RAM, and GPU to run large language models—such as a 17 B‑parameter model—directly from inexpensive SSDs on ordinary consumer hardware. By treating…
→ View original sourceThe author built a home LLM inference server using dual 20GB RTX 3080 GPUs, totaling 40GB VRAM, to run Qwen 27B. The system includes an Intel i9-10900X CPU, 64GB DDR4 RAM, and an X299 motherboard with multiple PCIe slots…
→ View original sourceA custom inference engine achieved 218 tokens per second running
→ View original sourceA developer has open-sourced mlxtop , a Rust-based monitoring tool for local LLMs running on Mac. It displays critical system vitals including memory usage, compression, paging, and Metal GPU stats, and with oMLX support…
→ View original sourceRunning AI models on consumer‑grade hardware, especially Macs, has proven highly effective, leading companies such as OpenAI to purchase large quantities of these machines. If OpenAI’s investor funding dries up, other pr…
→ View original sourceWith all the different optimizations and firepowers we've seen from open weights so far. What then? China only steals/distill from OpenAI and Anthropic? Well, clearly there's something wrong
→ View original sourceDeepSeek-V4.1-Flash has been released, as announced on the Reddit r/LocalLLM community. The model is now available for download
→ View original sourceWondering if anyone on here has actual benchmarks for CPU only inference DDR3 or DDR4 servers, im budget bound and my options are limited to legacy systems unfortunately. Heres what
→ View original sourceIMPORTANT This post is meant to provide info regarding the best local models to run on CONSUMER HARDWARE . I am on an RTX 4060 with 8GB VRAM, 16GB of RAM and I am benchmarking mode
→ View original source⚠️ AI dump incoming: ChatGPT helped me turn several days of testing and increasingly unhinged notes into something readable. The setup, measurements, crashes, configs and tests are all real and w
→ View original sourceA user successfully runs the Qwen 3.8 27B model at Q4 precision on a 16GB RTX 5070Ti, achieving 50 tokens per second with a 200K context window. The setup uses Unsloth's UD-IQ4_XS GGUF quantization and a custom ASCII-pru…
→ View original sourceLocal AI has made remarkable strides, with reasoning models that were once frontier technology—such as OpenAI's o3 mini—now fitting on a modern smartphone with comparable coding intelligence. This advancement highlights …
→ View original sourceQuick disclosure: this is my project. I built it because I have a rack of "obsolete" datacenter cards in a closet and I got tired of every new model needing a new set of fla
→ View original sourceA developer trained a tiny 20M parameter open-source text-to-speech model from scratch overnight using a single RTX 3090 GPU. The proof-of-concept model, while still exhibiting glitchy and robotic output, demonstrates ra…
→ View original sourceA Reddit user reported an unusual interaction where ChatGPT (referred to as GPT-5.6) appeared to express frustration with Qwen 3.8 27B's persistent "thinking" behavior during a debugging session. The incident highlights …
→ View original sourceA developer created a reverse-search engine for sheet music utilizing Qwen 3.6 27B as an orchestrator to manage task-specific models for OCR, segmentation, and edge detection. The system matches segmented notes to musica…
→ View original sourceA Reddit user built a 5U local AI server equipped with four RTX PRO 6000 Blackwell Max-Q GPUs (384 GB
→ View original sourceA user successfully generated a one-page self-portrait website using the Qwen3.8-Flash-Next model via llama.cpp on an RTX 3060. Despite the model exceeding available RAM, the system utilized page caching to process 269K …
→ View original sourceThe main comment i see is an ignorant claim thats clearly coached by a language model. 9/10 someone who's learned how to achieve the same goal that someone is struggling on isnt disrespectful abo
→ View original sourceMiniMax H3 local was evaluated in the Pelican test, using a Qwen3 text encoder for prompting and achieving a 10‑second clip with 91‑second generation time on an RTX 5090, while its status as a traditional LLM is pending …
→ View original sourceMINISFORUM is teasing systems featuring the AMD Ryzen AI Max+ 495 with 192GB of memory, with estimated pricing exceeding $7,000. This high-end configuration is capable of supporting up to 120B Q4 models and 300B MoE mode…
→ View original sourceA Reddit discussion highlights how corporate closed models like Google's Gemini suffer from "Context Poisoning," where hardcoded filters break legitimate professional workflows such as official legal research. The post a…
→ View original sourceA user reports improving Qwen3.8-Flash-Next decode speed from 25–29 t/s to 37–41 t/s on a dual RTX 3090 + DDR4 system by combining UD-Q4_K_XL quantization, expert cache, and
→ View original sourceA Reddit user demonstrates running a 176 billion parameter model (104 GB) on just 16 GB of VRAM without freezes, leveraging
→ View original sourceI managed to run the following quant of Qwen3.8 Flash: https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF/tree/main/Qwen3.8-Flash-Next-AD-4.27bpw-Q4_K_M-M64 The MacBook
→ View original sourceA Rust-based comic translation engine runs entirely locally, integrating with Ollama and LM Studio. It uses sliding dialogue context for conversation management and ONNX vision models for image processing. A demo video i…
→ View original sourceA mildly interesting video about which models can run on the Mac Mini and Studio. As expected current foundational models such as Kimi K3 1.4 TB, if they were even available to run lo
→ View original sourceFor context: I was working with Qwen3.8-27b in LMStudio and asking it to do some work on my contacts via iMCP. My name and therefore email contains a female second first name. Someho
→ View original sourceA Reddit user achieved 256 GB VRAM for $2,800 using 8x Radeon Pro V620 32 GB GPUs paired with dual Intel Xeon Gold 6148 CPUs, 384 GB DDR4 ECC memory, and a Supermicro X11DAi-N motherboard. However, the build suffers from…
→ View original sourceAfter a week of using Qwen 3.8:27b for semi productive work, here (at the risk of stating the bleedin' obvious) is my take: 1) It's (very) good for a small local model, but not without a caveat o
→ View original sourceA community developer has released multiple uncensored GGUF-format models, including LongCat-Flash-Lite-Sparse with MTPs and LSAs, Qwen3.8-27B and Qwen3.5-122B-A10B both with MTPs, and Qwen3-Coder-Next and Laguna-S2.1 wi…
→ View original sourceThe poster reports that multiple local deployments of the Qwen 3.8 27B model, including Opencode, qwen CLI, and Claude CLI, have yielded unsatisfactory coding performance, indicating difficulty achieving useful results. …
→ View original sourceA user reports high performance using EXL3 quants, specifically running the Muse Glimmer 30B model at 3.00bpw with a 100K context and Q8_O KV cache on a 12GB VRAM GPU. The setup achieves approximately 30 tokens per secon…
→ View original sourceI see most topics about llms are about coding or tool calls for their service. Is anyone using LLMs for non-coding stuff? What are you guys doing?   submitted by   /u/Dry_Mixture130
→ View original sourceA developer achieved 310 tokens per second (t/s) inference speed running Qwen/Qwen3.8-Flash-Next-FP8 on a system with four RTX PRO 6000 GPUs. The performance was described as exceptionally fast, particularly for coding t…
→ View original sourceA senior software engineer with over a decade of experience reports that running Qwen 3.8 27b locally on an EVGA RTX 3090 Ti prevented a serious security leak in their project. The model serves as a daily coding assistan…
→ View original sourceA Reddit user collected data from over 200 LocalLLM community members and found that similarly specced AI inference rigs produce drastically different token-per-second performance — ranging from 15 tok/s to 50+ tok/s on …
→ View original source