A test comparing Q8 and IQ3 XXS turbo4 quantized versions of Qwen3.6 27B shows that Q8 excels in API safety and input sanitization, while IQ3 XXS turbo4 performs better in thread management and modular code design. The model recommends merging both approaches: using Q8 for initial launch protection and IQ3 XXS for atomic writes and thread lifecycle, forming a combined Phase 1 foundation.
Qwen3.6 27B Quantization Performance Test Results
Best Settings for 48GB VRAM with Qwen 3.6 27B
A user shares optimized settings for running Qwen 3.6 27B with Q8_0 quantization on an RTX 4090 and RTX 3090 setup using llama.cpp. The configuration includes tensor split, 999 layers on GPU, 250k context, speculative decoding, and unified KV cache, achieving 75-100t/s throughput with vision and MTP support.
Alibaba announces Qwen 3.8 Max, a 2.4T-parameter model with open weights coming next week
Alibaba's Qwen team has announced Qwen 3.8 Max, a new 2.4T-parameter flagship model focused on coding, long-horizon agentic work, and multimodal reasoning. The company confirmed that open-weight versions of both Qwen 3.8 Max and the smaller Qwen 3.8-27B will be released next week.
Qwen3.8 open-weight release, Kimi Code CLI, Netflix LLM stack, Alibaba chip software
Alibaba announced the open-weight release of Qwen3.8, a 2.4-trillion-parameter model, while also open-sourcing its Zhenwu AI chip software stack to reduce reliance on Nvidia's CUDA ecosystem.
PrismML releases Bonsai 27B, compressing Qwen3.6-27B to 1-bit and ternary weights
PrismML has released Bonsai 27B, a low-bit quantization of the Qwen3.6-27B model that enables running large multimodal models on laptops and phones without retraining.
Qwen3.5 122B A10B at UD-Q2_K_XL fits in 64GB RAM and improves internal knowledge
A user demonstrates that Qwen3.5 122B A10B quantized with UD-Q2_K_XL can fit into 64GB of system RAM, replacing the previous best option for that constraint.