With our AI Workspaces, you can compare multiple LLMs side by side to find the right model for your use case.
At Google Cloud Next 2026 in Las Vegas this week, Google made a quiet but significant announcement: Gemini can now run on a single air-gapped server, fully disconnected from the internet — and from Google itself.
We ran Gemma 4 26B and Qwen 3.6 35B-A3B head-to-head on the same server, same quantization, same protocol. Gemma 4 is 3.7× faster at 32k context — and 7.2× faster at 128k. The gap widens with context, and the reason reveals something important about model selection for long-context workloads.
We put Qwen 3.6 35B-A3B on a developer laptop and a dual-GPU server. The speed gap grows from 2.4× to 5.3× as context grows — and the real bottleneck turns out not to be compute.
At Google Cloud Next 2026 in Las Vegas this week, Google made a quiet but significant announcement: Gemini can now run on a single air-gapped server, fully disconnected from the internet — and from Google itself.
We ran Gemma 4 26B and Qwen 3.6 35B-A3B head-to-head on the same server, same quantization, same protocol. Gemma 4 is 3.7× faster at 32k context — and 7.2× faster at 128k. The gap widens with context, and the reason reveals something important about model selection for long-context workloads.
