Gemma 4, Qwen3.5 vie for 36GB VRAM

36GB is the point where quantization and KV-cache behavior matter more than the headline parameter count; Qwen3.5 looks like the safer default for interactive agent work, while Gemma 4 is the more ambitious pick if you can tolerate heavier memory pressure.

• –Gemma 4 is Google’s most capable open family so far, with native tool use, multimodal input, and up to 256K context, but that also makes local inference easier to bottleneck on cache and latency.
• –Qwen3.5 has a broader release surface and stronger local-fit options around the 35B-A3B class, which tends to be easier to keep responsive on consumer GPUs.
• –For LM Studio and OpenClaw, the practical sweet spot is usually a quantized 26B-35B model with conservative context settings, not the biggest model you can technically load.
• –If the priority is agentic reliability and speed, Qwen3.5 is the pragmatic bet; if the priority is raw capability and you can tune around memory costs, Gemma 4 stays compelling.