Developer Reports 110K Qwen3.8-27B Context on RTX 5070 Ti
Community developer u/jayecin shared a llama.cpp configuration that sets a 110,000-token context for a custom-weight Qwen3.8-27B GGUF model on an RTX 5070 Ti. The developer reports generation at about 55 tokens per second, with roughly 1.5GB of VRAM to spare. The setup uses the Ryzen 9800X3D integrated GPU for display output, q8_0 and q4_0 KV caches, and flash attention. The report includes configuration and runtime logs, but the performance figures have not been independently validated.
The report provides a concrete community reference for running a long-context Qwen3.8-27B model on a 16GB desktop GPU, while documenting the custom weights, hardware and memory settings on which the reported result depends.
I wanted to share my setup thats working really well for my fellow 16GB desktop suffrage crew. I am able to get 110k context with ~1.5GB to spare and around 55 t/s on my 5070 TI.
First off I found this model https://huggingface.co/hitsfmdj/Qwen3.8-27B-GGUF-4.2BPW-16GB with custom weights that seems to be pretty good and only 11.8GB. However even with this model, I ran into issues with context windows greater than ~60k. Sometimes it would fit on the GPU and other times it didnt. It was a good size, but not enough for larger coding tasks and network automation stuff.
So I setup my computer to utilize my iGPU on my 9800X3D as my main display driver instead of the 5070 TI 16GB. Xorg alone was eating up 600-800MB of VRAM. The more applications I opened, the more VRAM I used the more problems/slowness I had. With my motherboard MSI MAG x870e Tomahawk, I can enable Hybrid Graphics which allows me to set the primary display driver as the iGPU instead of the 5070 TI but still utilize the 5070, reserving the full 16GB of VRAM for llama and gaming. I tested playing my main game Overwatch on my main display at 1440P 144hz and the game ran just fine at my FPS cap of 300. Ive been very happy so far with this setup, the extra context size has been a real game changer for longer and more complex tasks, its fast enough to persuade me away from ChatGPT.
#Qwen3.8-27B 4.2BPW GGUF#llama.cpp#NVIDIA GeForce RTX 5070 Ti#AMD Ryzen 9800X3D