Search: “models”

today was the last day of my subscription on ollama cloud, to be honest it was a great price value for me and with GLM 5.2 and Deepseek V4 Pro i was able to Vibe code my custom woocomerce shop with mu

Since now we have kimi k3 and next week we are getting Qwen 3.8 Max and also soon V4 pro Deepseek. I am curious if the old power house like Kimi 2.6/7 code and GLM.5.2 are all that relevant. especiall

A fresh llama.cpp PR (#26689) changes what looks like a tiny SYCL FlashAttention dispatch decision. With a quantized KV cache ('q4_0' / 'q8_0'), decode was being sent through the VEC kernel. On the au

I run the following on a 5090 and have been okay with its performance, it does most things somewhere 80-100 t/s, though that can slow down at full 262k context - more like 40 t/s at times. I use it pr

Following up on yesterday's post about running everyone's faves on 2 x 16gb cards while maximizing performance and KV. Previous post data used abandoned Cu130 VLLM image. Stats here are done on cu129-

J'ai consacré beaucoup de temps à l'optimisation de DeepSeek-V4-Flash-0731 GGUF sur une seule RTX 3090. Mon exigence absolue pour chaque configuration était la suivante : Le modèle doit rester utilisa

https://preview.redd.it/o6ik6qboeohh1.png?width=1134&format=png&auto=webp&s=4016f26c50c1d93bd3d0c7e880e9b55a2d75310f I have been running Qwen3.6 27b for a little while (mostly coding tasks) and recent

I cancelled my pro plan ealier because I wanted to use new Deepseek v4 flash 0731 which was available on Openrouter through API only (not yet on ollama cloud at the time). The old Deepseek v4 flash/pr

As for me, I own a system with an RTX 5090, Ryzen 9 9950X3D2, and 64 GB of DDR5. Every time I see research come out with a new way to train AI, I immediately think to try it on my system to see the re

Seen a ton of posts today about the DeepSeek API price hike. Half the feed is doom-posting, the other half is explaining basic GPU economics. Honestly, I get the cost side. Sub-cent tokens were never

Come on guys, we could get deepseek cheaper with more usage directly with them if we wanted. WHERE IS KIMI K3 WITHOUT EXTRA USAGE NEEDED !!? This is pissing me off like crazy submitted by /u/Other_Che

https://github.com/yhfgyyf/vllm-deepseek-v4-sm89 I couldn't believe that someone actually got vLLM working with this particular set of GPUs, but here it is. The video is from right after I got it work

It's the purple cluster on the top left (the good corner...) I'm running the MXFP4 version from Bartoswski with Dspark at 1K t/s prefill and 90 t/s gen (average). I tried different sampling params, yo

Llama.cpp currently uses cpu based sampling for user with mtp enabled. The PR moves sampling to the gpu, which on a 5090 boasts an 8% increase in tok/s for qwen3.6:35b. I tested it on my P40 and obser

Found this quant, so thought I would share, since its the best I've found so far for running on my mac (m3 ultra). It's got dspark/mtp support so runs faster than anything else I've tried. The tok/s o

Does anyone have an idea, when OpenAI will fix ChatGPT's glitched images? Since the 2.0 image generator was released, there is ugly glitches when you use generated image as a reference or just change

Originally, I was only getting around 140pp/s and about 21tg/s, but the config with -b 8192 -ub 8192 --cpu-moe is vastly superior, let's say 700pp/s and 18tg/s in the most relevant range. Test System:

Qwen3.8-Max (2.4T) is another massive contribution to the open weight community. On benchmarks, it performs closely to Kimi K3 and DeepSeek V4 flash across all categories and is better at coding and s

I've been following the recent pull requests and saw that the desktop app is being transformed from a chat-only interface into a full management tool with a tabbed settings UI, a model manager, and a

You have two choices here (in order of pref): Downgrade CUDA from 13.3 to 13.1 (skip 13.2 due to bugs) <- prefer this (thanks to u/fairydreaming for pointing this out) Use this vibed fork that works w

Hello, Also I want to join the hype of posting token specs. CPU: 2x Intel Xeon CPU E5-2650 v4 @ 2.20GHz RAM: 2x 4 Channel 2400MHz DDR4 GPU: 1x AMD Radeon 7900 XTX 24GB 3x AMD Instinct MI60 32GB Strang

Hello everyone I want to join the hype of posting specs. CPU: AMD EPYC 74F3 24-Core RAM: 8 Channel 3200 DDR4 GPU: RTX A6000 48GB Prompt processing is in the high 70t/s (got down to mid 30t/s at 300k c

We all have been there, tinkering around with models is fun but we rarely do it with research precision and issues are often subtle and hard to reproduce. There are a lot of benchmarks but running the

DSv4F doesn't ship a jinja, but for distributions that do and faithfully reconstruct what DS releases in their chat template python, every system message is hoisted into the system prompt at the top -

Hey everyone, I’ve built a Windows Middleware Agent Server designed to pair local LLMs (via Ollama) with frontends like Reins App. Key Features: Self-Healing Loop: If a generated PowerShell command fa

So I am in a conundrum and I'm thinking of asking for your opinion for the following: Currently, I have a 5800X3D gaming rig with a 7900XTX with its 24GB VRAM. It seems that for this subreddit, this c

Okay so this probably sounds like kind of a dumb setup but hear me out. I have a 4070 I’ve been running gemma4 off fine but I recently slapped in a spare 1650 I’ve had laying around to run a second li

I don't think anyone should quantize the KV with DS4F. I checked the the quality impact (PPL, KLD, Same TopP) for swhitching from BF16 KV to Q8 KV, and it appears significant. Very much in contrast to

Thanks to the community help I finally launched this llm. LM Studio refused to load weight onto second GPU but Unsloth Studio did so everything was done in there. Not a proper benchmark (used PC in pa

Here are my numbers: Quant Size Layout Decode Prefill Draft acceptance UD-Q8_K_XL 150.8 GiB 20 layers CUDA0 / 23 ROCm0 + drafter 44.0 t/s 564 t/s 0.535 UD-Q4_K_XL 144.4 GiB 22 / 21 + drafter 48.4 t/s

Antirez stealthily uploaded the new weights in the old folder... and there we were tapping our fingers. https://huggingface.co/antirez/deepseek-v4-gguf/tree/main submitted by /u/challis88ocarina [link

Wondering if it's possible to actually produce ai art works that are indistinguishable from authentic medieval woodcut illustrations like the one attached. All the AI attempts I've seen at re creating

What speeds are everyone getting with deepseek v4 flash 0731? I’m getting~200 tps prompt processing / ~11 tps token gen, on 4x5060ti16gb with ddr4 3200 ram at 4-channel, via llamacpp, with context win

I've ran Kimi-k3 through 34 oneshot prompts and evaluated the generated htmls, screenshots and gifs using sonnet 4.6. It came out to be better than opus4.8 from the evals. Kimi K3: https://oneshotlm.c

A100 with 40gb VRAM: 162GB Q8_K_XL ~16.1 tok/s generation Only 15.8GB of 40GB VRAM used with all experts on CPU NOTE just tested coding on linux box DeepSeek-V4-Flash-0731 runs losslessly on the singl

Source: https://x.com/deepseek_ai/status/2083084415157022911 & https://deepswe.datacurve.ai/ just combined data view. DeepSeek claims, not verified by DeepSWE yet. submitted by /u/sdexca [link] [comme

DeepSeek V4 Flash: Preview → 2026-07-31 Benchmark Preview 0731 Δ Terminal Bench* 56.9 82.7 +25.8 Toolathlon 51.8 70.3 +18.5 NL2Repo — 54.2 new Cybergym — 76.7 new DeepSWE — 54.4 new Agent Last Exam —

While waiting for some of the quants to drop, I load the API with $50 and ran it on SlopCodeBench Just vibe reading the results it seems like Opus 4.8 < Deepseek < Opus 5 https://github.com/michaelasp

It’s an MoE with ~3B active params and a 30B n-gram lookup table offloaded to RAM for fast 256k context on a 24GB GPU. Reminds me of Gemma 4’s PLE trick. Initial analysis suggest it wont be replacing

SenseNova released U1.5-Lite-Preview Benchmarks: Qwen-Image-Bench from 47.14 to 55.20. ImgEdit-Bench from 3.90 to 4.37. GEdit-Bench-en from 7.47 to 8.17. Key updates: 4K native generation with better

https://preview.redd.it/vxl4zslewigh1.png?width=1435&format=png&auto=webp&s=5a419870ca0cb13076be6c9ff4ef33d8177d5eda New version is 25% better than previous one and is near GLM-5.2 quality submitted b

Hi all, I have not seen this model talked about here but it seems like baseten (inference provider on OpenRouter) merged the vision encoder from Kimi k2.6 into GLM 5.2. I think the lack of vision was

pretty simple I did ollama launch claude, found out its slow asf, wanted to delete It and Idk how I have no idra if its the same as normal Claude Code uninstall or if it's a Little bit of a different

276B total parameters, 12B active, 1M context window. Blog post: https://thinkingmachines.ai/news/inkling-small/ NVFP4: https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4 GGUF's by Unsloth: h

Its a custom Swift/Metal inference engine that runs Gemma 4 26B-A4B-IT on M-series Macs with very low RAM. It uses ~2GB instead of ~14 GB. The result is reportedly 5–6 tok/s on an 8 GB M2 MacBook Air

I've tried grok and Claude's deep research mode and I was amazed with the speed considering the amount of sources analysed. Is there anything as fast that can run locally? My guess would be that to ru