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OpenAI co-founder and CEO Sam Altman recently sat down for an in-depth conversation at Stanford Graduate School of Business's "View from the Top" series, covering AI's development trajectory, safety challenges, organizational transformation, and personal growth. Altman admitted that OpenAI originall
A brand-new release from ISTA-DASLab with their GSQ-RCO smart quantization approach. Now DeepSeek-V4.1-Flash-1017 The quants are available on Hugginface: https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF Llama.cpp is not supported because the original architecture support is n
(Post Updated) PR: https://github.com/t-head/pla/pull/12 PR title: feat(gdn): support Qwen4.0-Max prefill heads T-head itself is alibaba oss: https://github.com/alibaba-oss Processing img 71x75uagq9xh1... Original Post: The accounts are <1 month old, these are either troll accounts or secret a
gigabytes of VRAM saved (Qwen Flash Next) "This is one of the things Strata does to squeeze the most juice from the experts' cache." submitted by /u/jacek2023 [link] [comments]
Before you read: I'm the developer of Basalt, one of the engines benchmarked below. I tried to keep this as fair as possible: same checkpoint, GPU and requests, and every engine set up the way its own docs recommend. How it was run is at the bottom. This post has had some AI cleanup from my o
Tried it on a build for gfx906(mi50 16gb) and ran worse than llamacpp on decode speed. submitted by /u/JsThiago5 [link] [comments]
NVIDIA-Nemotron-3.5-Bolt-E4B-4GB is a small language model (SLM) further post-trained from Gemma-4-E4B-it by NVIDIA. Nemotron-3.5-Bolt-E4B carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast lo
Lets see how difference is this from ISTA labs coder version https://huggingface.co/agentionai/Qwen3.8-Flash-Next-Gyro-Coder-GGUF/tree/main submitted by /u/soyalemujica [link] [comments]
Let's put things into perspective: Remember, for example, the xz utils ssh backdoor in 2024? They almost managed to pull it off, and that was in the most observed codebase of the planet. It was caught almost by chance by someone working on something unrelated. Read about it if you don't realize how
I've been noticing a lot of my downloads stopping suddenly and being unable to resume. When I checked the models it seemed like they were gone. Here's the ones I can see are missing: dealignai/MiMo-V2.6-Pro-RL-UNCENSORED audnai/penclaw-GLM-5.3-abliterated-for-offensive-cyber huihui-ai/Huihui-
I compared Qwen3.8-Flash-Next-W4A16-FP8PLE at xhigh reasoning vs DeepSeek-V4.1-Flash-1017-dsx-2.6bit (my own quant) at max reasoning by benchmarking using a modified version of BenchKit. DeepSeek V4.1 Flash 1017 2.6 bit scored 23/25, while Qwen scored 25/25 (full marks). This is just a small sample
submitted by /u/sensitivecrocodile [link] [comments]
I did little research, and chatterboxV3 and Fish-Audio-S2-Pro were the top result. Should I go with chatterbox? Or there are other better alternatives? submitted by /u/Cute_Praline_5314 [link] [comments]
On October 22, Chinese media reported that DeepSeek and MiniMax have officially completed the required registration filings under China’s new temporary AI export regulations. By submitting their technologies for cross-border review, both companies successfully fulfilled the administrative requiremen
I have 48GB VRAM (2x3090 24GB) and 48GB DDR4 RAM. If I get 128 GB DDR4 RAM can I run things like Deepseek V4.1 1017 and GLM 5.3 Flash at low quants with at least 20T/s or so and have it be better than Qwen 3.8 Flash Next etc? If not, is there another system I should get, maybe switch all the way to
Most of the speedup are touching along the likes like cutting the active params reletive to total params(MOE) but it does take a hit in stability & reliability compared to a dense-27b can be much more stable at low total params. We hade the innovation of MTP which is great and lots of other things l
InclusionAI just announced Sante too. Damn, two model announcements in one day. They're on a roll. I posted about the 3.1-flash-1019 update earlier today and they've already announced Sante. Two model announcements in one day, damn. This one's built on Ling-3.0-flash, with 124B total parameter
from inclusionAI: Introduction Ling-3.0-flash-Sante is our healthcare-specialized model built on Ling-3.0-flash. Its name comes from the French word santé, meaning “health.” With 124B total parameters and 5.1B active parameters per token, Sante is designed for medical reasoning, healthcare res
I've been building capy-flash, an open-source inference engine for running Qwen3.8-Flash-Next (125B MoE) and Qwen3.8-27B on consumer RTX 50 cards. Until now it needed my setup (two 16 GB cards and 96 GB of RAM) or better. v0.7 is about smaller machines: start with what you have, add a second card la
For anyone still downloading the last Flash: there's another checkpoint on the way. Ant Ling's announcement puts the 1019 version at 44 on the Artificial Analysis Intelligence Index, up from 41. They also report improvements on the agent evals: AutomationBench-AA goes from 62% to 68%, and AA-B
So, yesterday I made an apparently too simplistic post about this shitty model that everyone is cheering (probably bots, I would like to see what they achieved with it). Link https://www.reddit.com/r/LocalLLaMA/comments/1xb9hrb/tried_lfm2524a2b_its_crap/ The main critic is that I didn't provid
I recently got some more RAM on my desktop, which has opened up running larger MOEs by splitting them into RAM/VRAM. I know everyone loves Qwen flash next, but I'm curious what other ones are worth trying. On my list of to-try, I have: GPT OSS 120b (is this still usable nowadays?) Mistral 4 small
I asked a while ago if it would be worth it to try and run qwen flash next at q2 since I can only really fit it and nothing bigger, but I can run Q8 of 27B. Everyone told me it’s either a toss up or Q8 would obliterate it. Not the case. Now this is subjective BUT for complex, annoying, and super sub
I just downloaded Raon-1-mini 30B A4B as well as Flint 40B A2.5B and it just blows my mind that we have so many options now from so many countries. It feels like the countries are all working together and trying to contribute useful software and it’s just amazing. I can fucking TALK to my computer a
KittenML has just released code and weights of Kitten-TTS 2, which is a super-efficient frontier TTS model, and the world's first 1.58-bit TTS model. It runs in real time on just a CPU, with zero-shot voice cloning, multilingual support, and expressive / emphasis control. Code and weights are
This model looks really impressive. TL;DR -- Coding agent model. 40B params with 2.5B active. Resoundingly beats Qwen3.6-35B-A3B in TerminalBench 2.1 and SWE-bench Pro v2 (see HF repo for table), ties or betters it in the other benchmarks they chose to show. 3:1 SWA:full attention. 262k contex
I always feel a little bad asking LLMs to do research into itself or newer versions. It's like asking someone to face their own mortality, although that's anthropomorphizing. The user is asking me to investigate the system requirements for "Qwen 3.8 Flash Next" from at least 20 different sources. T
I'm sharing Mica v0.5, the follow-up to Mica v0.1. It's 4B and runs on one consumer GPU. On speed, it takes 19.8 ms per decision at 256 tokens and about 1.2 s at 32k on a single RTX 6000 Ada. That's up to 3.8x faster than the other 4B judges I measured, 12 to 19x faster than Perplexity's 27B d
I saw this today on X and decided to try it, I havent seen it posted here yet. I ran it on a single 3090 benchmarked it with qwen 3.8 27B before and after the optimization and saw these results: Observed prompt-processing throughput improved 22–30%; generation speed improved 1–5%. All tests pa
I bought a used PC with an RTX 3090 24 GB, 128 GB DDR4 and an Intel i9-9900X last year for under €2,000. With the release of DeepSeek-V4.1-Flash-1017 I decided to try running it on my machine. It is of course the smallest quant available (1-bit), but I was still happy to see it working at 7.2