I benchmarked 21 Qwen3.8 27B variants on 16GB VRAM
After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) onβ¦
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I benchmarked 21 Qwen3.8 27B variants on 16GB VRAM
After Qwen3.8 27B came out, I decided to benchmark the models that could fit in my GPU (RTX 5080) onβ¦
Ling-3.0-flash-VL, built on Ling-3.0-flash with visual understanding and visual agent capabilities
It performs well across visual perception, STEM reasoning, document intelligence, multimodal agent tβ¦
Georgi Gerganov on the Nvidia acquisition
Link: https://x.com/ggerganov/status/2095897173376618881 submitted by /u/CombinationKitchen76 β¦
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 handβ¦
Qwen3.8-27b is the first Local model im able to blindly trust
You know that thing where you just throw a task at a frontier model and not have to supervise it worβ¦
I built a server with 768GB VRAM for frontier, but all new frontier open source models are likely to be two trillion or above now, including next GLM 6, am I cooked?
This epyc server I am using twelve cards with 64 GB memory, plus 256GB ram. Looking at the most capaβ¦
NVIDIA's $12,930,300,000.00 acquisition of Hugging Face contains an easter egg. The first 6 numbers of the acquisition price represent the decimal conversion of Unicode character U+1F917. The π€ emoji.
From: Polymarket on π: https://x.com/Polymarket/status/2095646821485842805 Julien Chaumond on π: httβ¦
When you say, because I can. Limits of X870e
As the heading goes, at some point it stopped being about improvements and just whether I can. So chβ¦
Some people said the Minecraft clone I fully vibecoded with Qwen3.8-27B Q4 is not that impressive because Minecraft is in the training data, so I had the model add 4 things that are probably not.
submitted by /u/liright [link] [comments]β¦
Koboldcpp v1.120 released
submitted by /u/Fcking_Chuck [link] [comments]β¦
Qwen3.8-Flash-Next at 170K context on a single 96 GB card. ~110 tok/s.
I used the quantized n-gram to INT4, it's 32 GB, memory-mapped from disk. I confirmed that it works β¦
Qwen3.8-Flash-Next NVFP4 Day-3 support for 4xV100
RadixArk/Qwen3.8-Flash-Next-NVFP4 is now supported in SGLang-V100. 4 V100 32GB running full context.β¦