Search: “models”

Tibo said Tuesday, GPT-6 Sol is suddenly everywhere, and now Claude Opus 5.5 leaks are blowing up too. Yet… nothing so far. 😂 At this point I’m just refreshing X like it’s a live event. What do you th

https://preview.redd.it/ybwqqbrst0rh1.png?width=811&format=png&auto=webp&s=1e40c5b53e304caf2a10efa2baa6f998b9a9a0bb MiMo-V2.6-Distill-Qwen-9B is a 9B agentic model developed by Xiaomi MiMo through sup

# the What An engine to run Gemma 4 31B on blackwell under massive concurrency and rather specific workload patterns. I've been waiting for someone to do ninfer but for gemma, and, well, ended up havi

Hi all! After all the jev hype I wanted to try by myself and got qwen3.8 27b to mix-and-match open implementations on top of ninfer, and to be honest I'm quite impressed with the results, latency is n

been working on this thing called OpenComms for a bit. basically i keep multiple coding agents open at once, usually stuff like a lead, builder, reviewer etc, and i got sick of copying messages betwee

I have been reasonably satisfied with my single R9700 (32GB) as I can run practical quants of Qwen 3.8-27B at good speeds, as well as other similar models in its weight class (Gemma 4 is still my go-t

Released: Update your ComfyUI for workflow Weights: https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main Variety of Examples https://youtube.com/watch?v=tP_gGGJzEqg&feature=youtu.be https://model

I am trying to learn more about long-term memory and how people are handling that nowadays. Historically, I have used a mixture of paid cloud AI services (ChatGPT, Gemini, OpenRouter API) and my own l

Play it right in your browser, no download. Some of the GLB’s are messy, but overall I’ve enjoyed playing with it, and the last iteration made was the gun/weapon placement. Contributors are welcome, w

I’m one of the co-founders of NYX AI. It’s a Windows app that connects to local models through Ollama/lm studio or supported cloud providers, giving AI tools to work with documents, previews, spreadsh

Note: This writeup was put together with the help of AI. So I've got dual AMD MI50 32GB cards. If you know these cards, you know the story - AMD dropped official ROCm support for gfx906 after ROCm 5.7

I have this idea I'd like feedback on. most camera footage for training, is sunny daytime, because that's what cameras record most of the time. The edge cases models actually struggles with, like nigh

So I know that Ollama is built on top of llama.cpp, I decided to run a model on both and applied settings on llama.cpp like flash attention and q8/q4 kv cache. On Ollama (using the Python package) I w

MiniMax has released the source code for MiniMax Code’s terminal agent: https://github.com/MiniMax-AI/minimax-code https://preview.redd.it/st9izmm8zaqh1.png?width=3016&format=png&auto=webp&s=5853bc316

Ternary Bonsai 2 27B Tried it out and was excited. Gave it a prompt 'create a 3d little planet globe where I (player can walk around) and it has all these biomes to explore, the globe doesn't have to

We just released the first version of our Creative Writing benchmark, comparing 24 LLMs against human writers across 475 writing prompts. Creative writing is subjective, so the rankings aren't meant t

Anyone interested in trying out a new inference provider? We're testing Gemma 4:26b and Qwen 3.8:27B behind an OpenAI-compatible API and looking for a few users to give them a spin and tell us how it

Setup for one long agentic coding session (tools + vision) on a single 5090: Qwen3.8-27B RVN Heretic (ARA abliterated) at Q6_K, 159744 context, 1 slot, q8_0 K/V, DFlash2 speculative decoding, vision p

Follow up post of https://www.reddit.com/r/LocalLLaMA/s/nGMyKswrch. Thanks everyone who replied. I didn't change the specs. Might be loosing some of the memory bandwidth but will scale in future if I

I have tried using qwen 3.6 and 3.8 q5 64k context and I'm just not getting the experience everyone here seems to get. I'm using open code and it will loop, forget what it's doing, and mess up the ui.

Now that the dust has settled a bit - here's a write-up on running Qwen3.8-Flash-Next (125B-A6B MoE + 51B n-gram table) on relatively middle-tier hardware (RTX 4070 12GB + 64GB DDR5-5600 + Gen4 NVMe o

from internlm: We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre

I’ve been researching which models to sub to and I’m currently not subbed to anything. I’ve definitely searched here and other places for opinions but a lot of what I was reading wasn’t current or 6mo

I tried using deepseek v4.1 flash max subagents with astra extra high as an orchestrator. It was working perfectly with 1 until it suddenly stopped responding or doing work after 3 hours. Then trying

I have enabled the vision for the CIRU Strix UL4 quant of Qwen 3.8 flash next (others quants likely perform very similar) and tried it on a few things, then wanted to show my partner how great it work

We’ve been watching both models closely on InferX, and something interesting is starting to show up. GLM-5.3 Flash might actually be outperforming DeepSeek V4.1 Flash for some real-world workloads. To

Same 44-token prompt: low = 202 output tokens in 15s, max = 4,955 in 35s, unset = 10,579 in 68s. I'm using it via claude code with this: CLAUDE_CODE_MAX_CONTEXT_TOKENS=1048576 ollama launch claude --m

I wanted to see how my fully local home voice assistant compared to the latest GPT Live, so I tested it using the same conversation used in their 'Improved Intelligence' demo. In this video they ask t

I’ve been building 'Spomin', a router that replaces context with summaries directly in the KV cache. The goal is to keep long running sessions going without repeatedly stopping for full compaction and

Part 4 of the same box. Part 1 was 17 -> 25-29 t/s with the expert cache PR, part 2 was 37-41 t/s after switching to UD-Q4_K_XL and stacking MTP on the cache, part 3 was the top-k fallback that was so

I initially saw CED as just an efficiency improvement, but the more I read about it, the more it feels like an inference architecture leap. The encoder/decoder split has some pretty interesting implic

I’ve been using Qwen a lot recently and I’m honestly very impressed with it. For some of my tasks, Qwen seems to understand what I’m actually trying to achieve better than ChatGPT/GPT-5.6. I currently

Hi, I'm the co-creator of this Qwen3.8-Flash-Next engine support in MLX-serve. I've been tuning this one to run both fast, efficient and correct up 1m context using kv cache 8 bits in M5 Max 128GB. Qw

I don't think anyone posted about this here, but Qwen released a finetuned version of 3.5 4 for driving. The full Bf16 checkpoint is 9B. This is a very interesting development of Chinese AI labs tackl

I got 2x 20GB RTX 3080s + 128GB of DDR4 2666hz RAM (only 4 of 6 channels populated) + a Xeon 6148 I've always been a llama.cpp person and I've been running Unsloth's Q4_K_XL quant of Qwen 3.8 Flash Ne

TL;DR My TAK quant of Qwen 3.8 27b scored 82.81% on reasoning, comparted with 77.34% for the byte matched Unsloth UD IQ2_S and 83.59% for BF16. Edit: Some of you have tried coding with this reasoning-

I’ve been setting up an M1 Max Mac Studio (24-core GPU, 32GB unified memory) as a local LLM server and wanted to compare MLX vs llama.cpp on Qwen3.8-27B. I tried to keep the model footprint and benchm

Hi, I've been wanting to run my own local LLM for some time and I finally saved enough to get a budget GPU. I can spend 700 at most, and I'm looking for a GPU that can run quantized 30B~ parameter mod

Qwen3.8-Flash-Next (UD-Q4_K_XL) on a single RTX 3090 24GB + 128GB DDR4 — is this config optimal? Hardware CPU: Intel Core i5-12600K RAM: 128 GB DDR4 @ 3600 MHz GPU: NVIDIA RTX 3090, 24 GB VRAM OS: Win

I see a new one being launched every few days... How do these new harnesses compare to claude code, pi etc. has anyone switched from these? which harness to prefer and why submitted by /u/Background-J

How good/bad is it against comparable MoEs the same size? How does it compare against Qwen 3.6 35BA3B? Since we don't have 3.8 35B this seems like an upgrade if we look at some benchmarks like termina

Ollama has just announced, based on 'feedback', that they have switched to a credit model. If you're still on the old 20 Pro compute plan, DON'T SWITCH. I decided to calculate it before I risked click

I haven't had a chance to test it yet, but it looks very promising. It seems to speed up MTP for MoE models across different draft widths (especially greater than 1). Check the benchmarks. submitted b

Mac Studio, 96GB unified memory (M3 Ultra). I want the largest usable Qwen3.8-Flash-Next setup, and I'd rather not burn 100GB of bandwidth on the wrong download. Here's my math. Please tell me which p

I'm running Muse Glimmer 30B EXL3-SC 3.00bpw H4, fully resident on my 12GB VRAM GPU at 100K context with Q8_O KV cache. It's a joy to use a dense 30B model at this size and still get 30 tok/s on a VRA

I am doing a lot of translation work with different languages, and German is just exceptionally well done by Qwen 3.8 27B. It is lengths ahead of GPT-5.6 and even Fable 5. With the frontier models I o