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Qwen 3.8 Flash Next Can Build Funny Games

This is nothing impressive but, i had so much fun i wanted to share my experience with this model. (yes this post is written by human) I made an FPS with local Q4_K_XL 3.8 Flash Next (256k context) (i

Qwen3.8-27b is the first Local model im able to blindly trust

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DGX agent

You know that thing where you just throw a task at a frontier model and not have to supervise it worrying of it going off course? Qwen3.8-27b has officially gotten me to that point for local work. He

local-air-localllama

4 Sept 2026

Qwen3.8-Flash-Next: 256k context, 16tok/s on DDR4 and a Tesla T4

I've got an refurb Dell R740 running Proxmox that I put a Tesla T4 in, mainly to run some CTC local transcription work, but thought it would be fun to try DS4 when it came out, and it was appalling at

Skipping skills! First class tools... GLM-5.2

Claude Code is absolutely horrendous at managing docx XML structures and it's obscene that the best these frontier companies can do are skills. Skills are prone to breaking, burn tokens (hello busines

model-releasesr-ollama

Tossed distorted audio samples to an open-weight voice model; it did fairly well.

Being a person obsessed with testing new models that come out, times are really insane for me. Tested different kinds of TTS and voice cloning models but none of them gets it right in terms of emotion

UPDATE: Qwen3.8-Flash-Next on 2x3090 + DDR4 (Part 2): 25-29 -> 37-41 t/s decode (UD-Q4_K_XL + expert cache + MTP), plus a branch you can build

This is a follow-up to my post from yesterday (17 -> 25-29 t/s with the expert cache PR). Same box: 2x RTX 3090 on PCIe 3.0, dual Xeon E5-2696 v4, 188 GB DDR4-2133 LRDIMM, llama.cpp, full 261k context

Updated my benchmark with a new vLLM based recipe for Qwen 3.8 Flash Next : now up to 98/100 (instead of 91 previously)

I was using: weights https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4 with the optimized SGLANG (patched) from https://old.reddit.com/r/BlackwellPerformance/comments/1w04xb7/qwen38_flashnext_o

We open-sourced Paddock, our Rust/C++ inference engine with its own CUDA kernels (MIT/Apache-2.0)

I'm one of the developers. We said in August it would go open source in September and it did last night. MIT or Apache-2.0, pick one. The repo you see is our internal repo, kernels included, so from n

You can now run a 90M conversational LLM on the Sony PSP (hardware from 2004). Doesn't get more local than this.

Github link: https://github.com/thatblend/LLMPSP I wanted to see what the PSP can theoretically handle and I got my answer - a 90M model is about the max it can do without atrocious inference speeds.

AntLing open sourced Ling-3.0-flash-Fin, a finance-enhanced model for real-world workflows

Ling-3.0-flash-Fin is the first finance-enhanced model in the Ant Ling family. Developed by Ant Group with leading financial institutions and domain experts, it extends Ling 3.0 flash through continue

Best small LLMs for translations?

I am working with multi-language content and have so far been useing gemma4:e2b for general work. Today I tried it with Russian for the first time and the result was not great. Gemma4:e4b was better,

Can a 4B local model actually feel like an AI assistant?

I've been building Arcon around Qwen3-4B + LoRA. Instead of just making it a chatbot, I'm experimenting with persistent memory, personality/mood, internal state, tools, and eventually having it proces

Comfy H3 Sync Sound Challenge: The Winners!

Two weeks, one rule, and hundreds of entries from nearly 50 countries. Here's who took the four titles, and a look at everyone who made the final ten! Entries opened August 20 and closed September 1.

DeepSeek-V4-Flash vs. GLM-5.3-Flash on 2× DGX Spark

I've tried both and been having this debate with myself for the last few days, on two Asus Ascent GX10s (effectively the same as 2x DGX Spark): DeepSeek-V4-Flash-0731 (official weights) GLM-5.3-Flash

First results from H3 Acceleration Arena

local-air-stablediffusion

https://preview.redd.it/c8za3q7shanh1.png?width=900&format=png&auto=webp&s=5716c30be6913c351403fb16cc60a59e3b116200 https://huggingface.co/spaces/multimodalart/h3-acceleration-arena From author u/apol

Frontier models sabotaging local AI implementations?

For a few days I've been working on creating a custom local-only harness for some work related research using Codex / GPT 5.6 Sol and the model feels not only dumber than usual, but straight up counte

GLM5.3 Flash over DSV4 Flash?

I've been using Deepseek V4 Flash 0731 for a few weeks now and while I havent thrown it anything very hard, im quite happy with it. Using through antirez's great ds4 project. They've added support for

Google released TimesFM-3, a 330M-parameter time series foundation model with native multivariate forecasting (non-commercial license)

TimesFM-3 is the third generation of Google Research's zero-shot forecasting model, and the main change from 2.5 is that it handles multivariate inputs natively instead of being limited to a single se

I do not wish for Nvidia to have a majority stake in Hugging face.

I like Nvidia, Ollama, and Hugging Face. I wish each of them well. But one owning the other creates a huge conflict of interest. Separation of Powers is key for long term sustainability of open weight

I finally am ditching Nano Banana thanks to H3

If you are like me and use Google flow for NB2/NBP there is hope to finally ditch it. Let me start off by saying I’m not very impressed with any of the current local image edit or Image Reference mode

I released sanoTTS: smallest complete TTS stack in 294k params (337 KB) that runs on $3 microcontroller and a 1.46m one that beats models 3x and 10x it's size

I have been trying to squeeze TTS stack down far enough to run in a $3 chip which has 512kb of SRAM without NPU. While trying to get to that milestone i built sanoTTS which has - 11 voices, 6 language

IFM/K2-Horizon-MoVA-36B-A4B-GGUF · Hugging Face

more sizes (probably still uploading): https://huggingface.co/IFM/K2-Horizon-32B-GGUF https://huggingface.co/IFM/K2-Horizon-7B-GGUF https://huggingface.co/IFM/K2-Horizon-3.7B-GGUF https://huggingface.

Increasing active parameters per token in MOE (Qwen 35B A4B+) reduce reasoning token by 8.5% - and you don't need to train or finetune!

I want to share a short paper just published exploring a simple but surprisingly effective optimization for sparse MoE reasoning models. The idea: Instead of retraining anything, we just tweak the rou

Introducing Quartermaster, an open source local AI platform designed for ease of use that does not sacrifice customizability

It started as a fork of llama-swap, but I have been building it out for myself since then as a convenient tool for all my local AI needs, and by now it has drifted far enough to be its own thing. The

Krea2 Turbo Distill 4 step LoRA - new checkpoint (chk60K) released (fine detail now above the 8-step teacher and clean of artefacts, best prompt-adherence and teacher-faithfulness scores so far)

Krea 2 Turbo — 4-Step Distillation LoRA (work in progress) A LoRA for Krea 2 Turbo that reduces the minimum usable step count from 8 to 4. ⚡ Half the steps — 8 → 4, on Turbo's own deployment sigmas. ⏱

Looking for Captain Obvious

I might be missing something obvious, but when I open any cloud model, by default I'm dealing with the 200k version and the first thing I need to do is /model to change it to the 1M version. Is there

model: add NVIDIA Nemotron-3-Puzzle-75B-A9B (NemotronHPuzzle) support by YanissAmz · Pull Request #25444 · ggml-org/llama.cpp

75B MoE is an interesting size to check, you can run it today (no MTP support yet) The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super,

Hardware

'ModelScope' Is a Hugging Face Alternative now that Nvidias deal is a Go

I liked the Nvidia that focused on just GPUs for gaming, not on the Nvidia of today which seem want power consolidation. Modelscope is another platform for those that simply want to know an alternativ

hardwarer-localllama

Multiplayer world simulations with h3 max

local-air-stablediffusion

Discovered that using a realtime llm + h3 world simulation, you can allow multiple different simultaneous character instructions that are restricted to only controlling their respective character. If

My RULE of Thumb of choosing a models

This is mostly for setting up for expectation, since personally without LLM i could take 3 days (15 hours of active programming) to debug or implement a feature, but with Qwen 27B (even before Qwen 3.

Need to decide: DGX spark vs framework desktop vs Mac mini/studio

I’ve been running qwen on my personal Mac but I’m getting to the point where’d I’d like to have something always on, running various jobs, and some more ability to experiment and earn about fine tunin

New ollama plan usage

Bruh wtf, i switched to new method now im pissed, 20 dollars a month for 60 api cost a month ?? Open code provides this for 10 dollars a month , and if I use a beast like kimi k3 I can drown my months

Ollama Pro is 20, but the new '60 credits' are more like $7-8 elsewhere

Everyone is talking about how the new credit system gives significantly less usage than the old Ollama Pro limits, but there's another part of the pricing that seems a bit crazy to me. For example, I

model-releasesr-ollama

Hardware

Open-sourced an experimental standalone DLSS 5 video player for neural rendering

hardwarer-stablediffusion

I’ve been experimenting with neural rendering outside a game engine and built a native Windows video player around it. It prepares a neural-rendered version of a video, caches it, and lets you switch

Openvurp: a wallet of agents that work together

I have a few AI subscriptions and I kept running into the same problem. I had powerful models available, but I struggled to use them in real life in an organized way. I looked at projects like OpenCla

model-releasesr-ollama

Qwen 3.8 27B Vs. Qwen 3.6 27B on oMLX

Quality: 81.1 → 87.7 (+8%) Speed: 35 → 29 tok/s (−16%) Runtime: 8m51s → 44m39s (5x longer) Output tokens: 18K → 78K (🤯) Noticeably better quality, but you're paying for it with tokens and time. Full b

Qwen-3.8-Next-Flash Ngram Hot-Swappable Knowledge Injector for llama.cpp

Looking into the new Qwen architecture, I was curious if you could modify the Ngram PLE Table to make it work like a long-term knowledge database. It turns out that, with some limitations, you can. I

Qwen3.6 35b Q2_XXS: Being GPU poor in 2026 is not so bad

A potato can create a very cool RPG in 24 minutes Laptop I3 8gb RAM 0gb VRAM, Windows 11 llama-server.exe --host 0.0.0.0 --port 8080 -m modelsqwen3.6-35b-InstructQwen_Qwen3.6-35B-A3B-IQ2_XXS.gguf -c 8

Qwen3.8-Flash-Next MTP merged in ik_llama.cpp (integrated head or separate -md file)... 45 → 90 tok/s on a 5090 + 128GB, works down to a 12GB 4070

ik_llama.cpp merged qwen4exp MTP support yesterday (PR #2369, mine, reviewed and tested by four other people on their own hardware). It's on main now, no fork or patch needed. Posting since the last c

Qwen3.8-Flash-Next on 2x3090 + DDR4: 17 → 25-29 t/s decode with the expert cache PR

Sharing some numbers because most posts on this model are either using a single 3090 or unified systems from what I've seen. My current setup: 2x RTX 3090 (PCIe 3.0), dual Xeon E5-2696 v4, 188 GB usab

[RELEASE] - SupraGDN-5M - a tiny GatedDeltaNet model competing with other community while models trained on MUCH less data!

https://preview.redd.it/5rs3d7vtgbnh1.png?width=950&format=png&auto=webp&s=df828c1146bc6167bd09d3300018aba426fc1ab2 Hey, r/LocalLLaMA ! We are releasing SupraGDN-5M. It's a tiny GatedDeltaNet (GDN1) b

Repodify, a fully local & opensource podcast summarizer, or BYOK if don't have GPU.

Disclaimer: I'm the builder. --- Over the past 2 yrs, I was working on a SaaS ML project & got very interested in ML/DL/AI. As everybody else, there were some normal paths I took to build a solid unde

Testing Deepseek-V4-Flash-Vision-EXP on Dual RTX6000 build

Upgraded my work LLM server from DSV4-Flash-0731 to the new Vision-Exp model and did this little test for fun to see quality comparison. Test was performed in Openweb UI connected to my vLLM container

Testing orangesouth/MinimaxH3CinematicRealism and vpakarinen/better-human-motion-h3-lora 'dean winchester vs sub zero' pruned fp8 model no turbo lora 32steps

https://huggingface.co/orangesouth/MinimaxH3CinematicRealism/tree/main https://huggingface.co/vpakarinen/better-human-motion-h3-lora/tree/main prompt integrated_multimodal_description: dy, [Shot 1] Ph

Training-free hack to reduce MoE reasoning tokens by 8.5% without losing accuracy but increasing Active parameters per token in MOE (Qwen 35B A4B+)

Hey everyone, Wanted to share a short paper we just published exploring a simple but surprisingly effective optimization for sparse MoE reasoning models. The problem: MoE reasoning models (especially

We built an open-source, model-neutral agent harness and compared it with claude managed agents - for the same model, got same accuracy, upto 75% lower cost

We have been working on an open-source, model-neutral agent harness for general purpose agents called TrueForge, and wanted to understand how much the harness itself actually matters. So we ran 14 tas

We need a better taxonomy for what people are calling 'continual learning'

Continual learning isn't some fake term but a real goal and arguably one of the most important open ones in 2026. It roughly means: ‘building systems that keep acquiring useful knowledge or skills aft

What are your best sources to learn about LLM inference?

The job of doing inference is not the same for every model ou there, as it depends on the architecture and configurations and the available machines. Many times we would not have the right machines to