Qwen3
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Aug. 17, 2026 / Featured
We Tested Qwen3.8 27B: How Much GPU and VRAM Do You Really Need?
Qwen3.8 27B is another model in the 27B class that looks particularly interesting for local inference. We wanted to find out what it actually takes to run it on consumer hardware, especially at longer context lengths. This article is based on our own llama.cpp benchmark results. We are not evaluating model intelligence, coding quality, or...
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Feb. 26, 2026 / Hardware Insights
Qwen3.5 27B and Qwen3.5 35B: What Hardware Do You Actually Need? (GPU Benchmarks Inside)
Qwen3.5 27B fits comfortably on a 24 GB GPU up to 131k context in 4-bit, but becomes memory heavy at 262k. Qwen3.5 35B MoE in 4-bit is the more practical long-context model for 24 GB cards, and it is significantly faster in token generation despite having more total parameters. VRAM is still the main constraint,...
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Feb. 4, 2026 / Hardware Insights
Qwen3 Coder Next 80B A3B: what it takes to run it locally
Direct answer first: Qwen3 Coder Next 80B A3B is one of the most hardware-friendly 80B-class coding models released so far. Thanks to its MoE design with roughly 3B active parameters, a single high-VRAM GPU can run it at full 256k context, and even dual consumer GPUs can handle the 3-bit version comfortably. VRAM, not raw...
