Techyon - AI News Aggregator

Qwen3.6 27B Pure Quant: 40 tok/s on 16 GB VRAM

Hello everyone! I want to share the result of my experiment to make **Qwen3.6 27B** **Q4\_K\_M** fits in to my RTX 5060 Ti 16 GB. Inspired by u/Due-Project-7507's work on [Ununnilium/Qwen3.6-27B-IQ4\

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Qwen3.6-35B-A3B Q4 262k context on 8GB 3070 Ti = +30tps

..and on 8GB VRAM I can even push the context to 320K, 400K, 512K, and yes.. 1M. But it does start to slow down noticeably beyond 150k so I'd only do this if I ever really want the larger context. Th

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ByteShape Qwen3.6-35B-A3B: 30% faster than Unsloth IQ on 6GB VRAM laptop

A few days ago I posted about my experiments with MTP on a 6GB VRAM laptop. That didn't work so well; CPU offload hurts MTP performance badly. But now I've tried out the [new ByteShape quants](https:/

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Experimental "Preserve Thinking" Jinja Template for Gemma4 31B in llama.cpp

[https://huggingface.co/stevelikesrhino/gemma-4-31B-it-nvfp4-GGUF/blob/main/gemma4-improved.jinja](https://huggingface.co/stevelikesrhino/gemma-4-31B-it-nvfp4-GGUF/blob/main/gemma4-improved.jinja) Ya

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meituan-longcat/LongCat-Video-Avatar-1.5 · Hugging Face

# 🚀 Model Introduction We are excited to announce the release of LongCat-Video-Avatar 1.5, an upgraded open-source framework that prioritizes extreme empirical optimization and production-readiness f

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OpenBMB presents the model BitCPM-CANN 1.58 bit

Se están probando los modelos nuevos en el Huawei Ascend 910B Link : https://x.com/i/status/2057816337880355220

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First AI to Beat Every Human in a Programming Competition - Agentic GRPO Explained

* Traditional RL for LLMs treats one answer as one trajectory: * prompt > reasoning > final answer > reward * Agentic systems are different: * they call tools * generate hypotheses

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Can't believe I got it working! Dual GPU - 48gb VRAM llama-cpp server - R7900 + 7800XT

Setup: Kubuntu 24.04 - AMD cards - R9700 AI PRO and 7800xt (32gb + 16gb) - llama-cpp server - stack setup in docker - vulkan image I tried with ROCM but it wouldn't play nice with RDNA4 + RDNA3 mix.

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Qwen-27B-IQ4_KS for ik_llama.cpp, especially for NVIDIA with 16GB VRAM

Hi everyone, I'm presenting a new quantization of the Qwen-27B model, created specifically with 16GB VRAM NVIDIA GPUs in mind. I used quants that, unfortunately, are not yet available in the main ups

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G4-MeroMero-26B-A4B-it-uncensored-heretic Is Out Now, a Finetune of gemma-4-26B-A4B-it, With KLD of 0.0152 and 12/100 Refusals!

G4-MeroMero-26B-A4B-it-uncensored-heretic: A Low-Refusal Finetune of Gemma-4-26B The model community has received G4-MeroMero-26B-A4B-it-uncensored-heretic, a newly released finetuned derivative of gemma-4-26B-A4B-it. Th…

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NVIDIA Removes Gaming Revenue Category From Financial Reports

NVIDIA Financial Reporting Restructuring NVIDIA Restructures Financial Reporting: Removal of Dedicated Gaming Revenue Category NVIDIA is undergoing a shift in its financial reporting structure, removing the dedicated rev…

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BeeLlama v0.2.0 – major DFlash update. Single RTX 3090: Qwen 3.6 27B up to 164 tps (4.40x), Gemma 4 31B up to 177.8 tps (4.93x). Prompt processing speed near baseline.

BeeLlama v0.2.0 Unveiled: Massive DFlash Optimization Drives 4.4x+ Acceleration on Local LLMs BeeLlama v0.2.0 introduces a major DFlash update, significantly boosting inference efficiency for large language models (LLMs)…

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DeepSeek is pushing forward with $10.29 billion financing round, with Liang Wenfeng committing to continue developing open-source AI models rather than pursuing short-term commercialization goals

DeepSeek Advances $10.29 Billion Financing Round, Committing to Open-Source AGI Development DeepSeek has announced the advancement of a substantial $10.29 billion financing round. Crucially, founder Liang Wenfeng has dec…

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