RTX 4090
-
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...
-
Apr. 3, 2026 / Hardware Insights
What Hardware for Gemma 4 26B and 31B LLM Local Use
The new Gemma 4 models from Google DeepMind have landed, and for local LLM users this is one of the more practical releases in a while. The lineup gives us two interesting mid-size targets: a 26B MoE model (A4B) and a 31B dense model. Both support up to 256K context, tool calling, and personal agent-style...
-
Nov. 3, 2025 / Hardware Insights
Inside PewDiePie’s $41,000 AI PC: 424GB of VRAM for Local LLMs
When one of YouTube’s biggest creators decides to build a personal AI supercomputer, the local LLM scene takes notice. PewDiePie’s journey into AI hardware has produced a multi-GPU, 424GB VRAM workstation that many enthusiasts dream of. While his budget is far beyond the average builder, his component choices and setup offer a valuable blueprint for...
-
Oct. 17, 2025 / LLM Benchmarks
RTX 4090 LLM Benchmarks: Performance Across 4K – 131K Context Sizes
I tested the RTX 4090 with five quantized models to measure real-world inference performance for local LLM workloads. This is the second article in my GPU benchmark series, following my recent RTX 5090 tests. I ran these benchmarks to provide concrete performance data across different model sizes and context lengths using llama.cpp. Testing Environment My...
