Reddit user reports mixed-vendor GPU node running three Qwen vision models
A Reddit user reported that the lvgale node combines AMD RX 580 and RX 570 GPUs with NVIDIA GTX 1660 Super and GTX 1070 GPUs around an Intel Core i7-6700K. Its custom Nuitka-compiled Python engine, Slipstream, reportedly used a mixed-vendor Vulkan backend and Flash Attention to concurrently serve Qwen3.8:27b-vision at 3.82 t/s, Qwen3.5:9b-vision at 4.75 t/s, and Qwen3.5:2b-vision at 12.47 t/s. The post also claimed stable vision-processing performance across the mixed AMD-NVIDIA topology, but the measurements and stability claims were not independently verified.
The report presents Slipstream as an experimental orchestration approach for running multiple vision models across mismatched AMD and NVIDIA hardware despite constrained PCIe links.
Everyone loves a clean, unified server rack, but my standby node (lvgale) is an absolute physical abomination. It is a Frankenstein collection of mismatched parts and bolted-together VRAM that has no business running efficiently.
The Hardware Setup (lvgale)
The Software Engine
Concurrent Inference Performance
In all cases seamlessly handling OCR and complex vision parsing.
While the 2B model runs slightly faster on a modern laptop iGPU due to unified memory, keeping a 27B model stable across this mixed-architecture, low-bandwidth topology without crashing the Vulkan driver is a massive win. For deeper technical architecture details, the engine is documented at slipstream-llm.com. submitted by /u/FieldMouse-AI
[link] [comments] |
#Slipstream#Qwen3.8:27b-vision#Qwen3.5:9b-vision#Qwen3.5:2b-vision#AMD RX 580#AMD RX 570#NVIDIA GTX 1660 Super#NVIDIA GTX 1070#Intel Core i7-6700K