Model Releases
1x32GB V100 vs 2x16GB V100 vs 5060ti 16GB for QWEN 3.8
Hi All, I am currently contemplating an upgrade from my 5060ti 16gb. I am getting ~40t/s with 130k context on Qwen 3.8 IQ3_S HF quant. I am running llama.cpp on linux. Objective is to increase context
Hi All, I am currently contemplating an upgrade from my 5060ti 16gb. I am getting ~40t/s with 130k context on Qwen 3.8 IQ3_S HF quant. I am running llama.cpp on linux. Objective is to increase context and use a better quant and also free up 5060 for other tasks. The options I am considering are 1x32GB V100 and 2x16GB V100. Theoretically, 2x16GB should be superior in terms of performance to 5060 and 1x32gb due to higher memory bandwidth. One issue I have to deal with is that I am limited in terms of CPU to GPU comms - I only have 2x x4 lines available. Any other good options in the same price range? UPDATE: Found a very interesting page, showing performance of multiple V100 with qwen 3.8 : https://domoticx.net/docs/llm-with-lama.cpp submitted by /u/ColorsOfCosmos [link] [comments]
Related
- Qwen 3.8 27B at 50 tok/s with 100k Context on a 16GB GPU! (beellama.cpp)
- After pushing 1M+ tokens through Qwen 3.8 27B, here is my optimal llama.cpp config for 16GB VRAM (73k Context, Agentic Coding)
- Qwen 3.8 27b in 24gb of VRAM
- [[benchmark-optimal-dflash2-quants-for-speed-and-context-size-|[Benchmark] Optimal DFlash2 quants for speed and context size, 5090 RTX, llama.cpp, Qwen 3.8 27B Dynamic3 Unsloth. Comparison with MTP]]
- Has anyone actually made 64k feel like 300k+ with recursive local agents?
Source: r/LocalLLaMA | 2026-08-30