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I was taking a look at Kimi K3 scores on the Artificial analysis leaderboard and was quite baffled when I saw this chart. Granted, Deepseek has always been the king of price to performance, but this is still incredible. Is it just API subsidization or have they optimized their models truly thi
We’re building a robotics data platform with a lot of images, video, and text metadata. For search, we use SigLIP 2. GPUs handle batched asynchronous image/video embedding and indexing, while this small Rust + ONNX Runtime service handles live text queries on CPU. Both land in the same embedding spa
one-shot. 3.5 hrs. that's insane. play with it here: https://macos27.kimi.page/ Original X post: https://x.com/mweinbach/status/2077878247920951400 that's insane. submitted by /u/AsejereDaDeje [link] [comments]
I have three Radeon AI PRO R9700s. I wanted to know if I could tune a quant for this specific hardware instead of just using the generic upstream formats. Came across the Github project for ROCmFPX by the wonderful Carlo Pasquale and started digging in. The fastest variant I've used is the MTP versi
New day new model....... can't wait to see in a few weeks how Minimax 3 Pro (2.7T parameters) and GLM 5.3 reinforces the narrative. As a group, all of them are accelerating their progress. THAT is the strength of open source. Meanwhile less enterprises are going to trust Anthropic and OpenAI. Why?
so part of what inspired my benchmark post was that it does seem like folks here are generally converging on "unless you are able to operate at very large scales with a lot of system RAM and VRAM, the best model for code work is generally Qwen3.6-27B." what I'm hoping is that we have some good place
submitted by /u/Qwen30bEnjoyer [link] [comments]
I've seen the benchmarks, they supposedly right up there with Fable 5 and Sol 5.6. Howerver I'm skeptical of benchmarks and the kimi k3 creators even mentioned the user experience isn't quite on par. How is K3 doing on coding? How is the personality? Does it's thinking and train of thought feel hi
TL;DR: I ran DeepSeek-V4-Flash through the same 89-task Terminal-Bench 2.1 suite on two very different local setups: an aggressively quantized 80.8 GiB GGUF on one 128 GB M5 Max MacBook; the native mixed FP8/FP4 checkpoint with DSpark speculative decoding on 2× DGX Spark. The Mac finished at 47/87
submitted by /u/Charuru [link] [comments]
TL;DR: This post is part update, mostly thank you for your support :)) Observer is an open-source app that lets local LLMs watch your screen and notify you (WhatsApp/SMS/email/Discord) when something happens. A year of your feedback later, setup went from "flaky and very manual" (your words, and fai
So I know this is going to incur the wrath of the Qwen cult but after a month of using 27b q8_0 as my primary coding agent in 6+ agent coding workflow with GPT-5.5 as the orchestrator, I got very frustrated with the amount of back and forth I was doing just to always end up in the same place w
Hey all! Wanted to see how DeepSeek V4 Flash GGUFs in two different quants perform on my hardware and share the results. Tested two quants on the fairydreaming/llama.cpp dsv4 fork. As a bonus, I also ran the same model (IQ3_XXS-AS) on mainline llama.cpp b10064 just to check. TLDR: turns out the fork
I’ve been seeing a lot of people talking about small models (~7B), and I discovered them when I started running models locally on my MacBook Air M1 (8GB). I’ve realized that 99% of the time, we don't actually need massive 1.3T models like Kimi k3, nor do we need to pay a ton of dollars for a Claude
After the recent llama.cpp changes, DeepSeek V4 Flash has become much more usable. I ran some benchmarks and wanted to share the results along with the config I used. I'm using DeepSeek-V4-Flash-UD-Q8_K_XL from Unsloth: https://huggingface.co/unsloth/DeepSeek-V4-Flash-GGUF Config: llama-server \ -m
This is a OnePlus 15R with about 11GB of usable RAM. The heaviest model is gpt-oss-120b, Q4_K_M, 60GB on disk. So it's roughly 5x bigger than the memory it's running in, which means keeping it resident isn't a matter of tuning, it just can't happen. It runs anyway: 1.3 tok/s at the model's own
Basically the title. We had our last drop in the beginning of April, do we just not get a refresh of Gemma or Qwen? submitted by /u/Aggravating-Push-207 [link] [comments]
submitted by /u/SporksInjected [link] [comments]
Howdy folks, Long time reader here. This time, I would like to share my experience with this model that I have been cautiously optimistic about when I saw the announcement post on this thread. ----- My setup: AMD Ryzen 5 on AM5 chipset (I genuinely don't remember the exact CPU. Just pick whatever
PrismML built Bonsai on top of Qwen3.6-27B by quantizing the weights down to 1-bit. That takes it from ~54GB to 3.9GB, small enough to fit and run on a phone, while keeping ~90% of the benchmark scores It's true binary quantization ("binary g128") - every weight is a single sign bit and each
I just saw that these had dropped. Still very much early days, but nice to see a new locally runnable foundation model, along with a couple of thinking preview versions. Anyone taken a look at this yet? Am intrigued to see how it holds up compared to Qwen 3.6 and Gemma 4 (my current stack)
Axios: China just erased America's AI lead: https://www.axios.com/2026/07/17/china-ai-kimi-k3-open-source-anthropic-opus submitted by /u/Nunki08 [link] [comments]
Some of you might remember that I posted some time ago about the GGML-ported asset production pipeline. A key elelent of that was the TRELLIS.2 port that performs image-to-3D generation. Well, I'm happy to report that after a grueling debugging session (thanks to https://www.reddit.com/user/Ia
https://preview.redd.it/8ank6w5gardh1.png?width=1745&format=png&auto=webp&s=3c6c25261feaa6ecfe82d23ef627dfb43181e6e9 Huge respect to the Kimi K3 team for putting this sentence right at the beginning of their launch post. Instead of pretending to be the best, they openly acknowledged that K3 st
From Vincent Chow on 𝕏: https://x.com/vince_chow1/status/2077947375964791028 submitted by /u/Nunki08 [link] [comments]
Past April of 2026, all open-source LLMs have been in the 0.5T+ terrirory: MiniMax M3, Kimi-2.7-Code - now Kimi-3 (2.8T), GLM-5.2, Inkling by ThinkingMachines is also a 1T model and perhaps some more models I forget now. If chinese labs find it more profitable (I would not accuse them since it takes
submitted by /u/pscoutou [link] [comments]
Ran a quick test sweep across the frequency range of the P100s using Darwin-36B (Qwen 3.6 35B-A3B sort of). Not a comprehensive test. Feel free to share your power efficiency tips, maybe we make a bigger thread as we explore ways to improve our efficiency, not just raw tps. Try it: sudo nvidia
I just have to show someone other than the guys at work who think I'm crazy. I've been working on this app since about early June of 2025 and while it has come a long way in its workspace tools, I had always only had basic web capability. But I saw some posts yet again recently about SearXNG s
If history is any indication, open-source models in the 27B dense range should have caught up to what the US government banned two weeks ago because they thought they were too dangerous in less than half a year from now. Qwen 3.6 27B outperformed models that were considered frontier models onl