MiniMax-M2.7 vs Qwen3.5-122B-A10B for 96GB VRAM full offload?!
Benchmark tests show Qwen3.5-122B-A10B delivers 2x better coding accuracy and faster inference.
A detailed benchmark by Reddit user VoidAlchemy, conducted on a powerful rig with 96GB of VRAM (2x A6000 GPUs), has provided a clear recommendation for developers running large language models locally. The test pitted two quantized, open-weight models against each other: ubergarm's MiniMax-M2.7-GGUF (IQ2_KS, 2.622 BPW) and ubergarm's Qwen3.5-122B-A10B-GGUF (IQ5_KS, 5.441 BPW). The primary goal was to determine the best model for local 'vibecoding'—a term for AI-assisted programming—when fully offloading to GPU memory.
On the critical HumanEval benchmark for code generation, the results were decisive. The Qwen3.5-122B model achieved a pass@1 score of 0.494, more than double the MiniMax-M2.7's score of 0.220. While inference speeds were comparable (31:20 vs. 32:48 for the benchmark), the Qwen3.5 model also won on quality-of-life features. It supports a full 256k unquantized KV-cache and includes an mmproj for image processing, whereas the MiniMax model requires a heavily quantized KV-cache to fit a 160k context. Despite MiniMax-M2.7's support for speculative decoding, the overall performance and usability crown went to Qwen3.5-122B-A10B for this high-end hardware configuration.
- Qwen3.5-122B-A10B scored 0.494 pass@1 on HumanEval, more than double MiniMax-M2.7's 0.220 score.
- Testing was done on a 96GB VRAM rig using GGUF quants from ubergarm, with models fully offloaded to GPU.
- Qwen3.5 offers better quality-of-life with a 256k unquantized KV-cache and image support, edging out MiniMax's speculative decoding.
Why It Matters
Provides a data-backed hardware recommendation for developers investing in high-end local AI coding assistants, saving time and cost.