New: Flash-Next versus Qwen3.8-27B · Seven tested packages, workload-specific results and the full data in the benchmark explorer.
FROM THE LAB. TO YOUR SETUP.
Hands-on local LLM benchmarks, practical AI workflows, and the hardware behind them.
Watch the tests. Explore the evidence.
I want to know whether a model can do useful work on hardware I can actually run.
QuantBench helps me see which tasks it handles and where it falls apart. HomHaystack checks whether it can find and use details in a long context. I look at both, along with memory use and how long I’m waiting for an answer.
The setup matters. I keep the quant, thinking mode, context, cache, and benchmark edition with the result. An unfinished run stays unfinished. A projected fit on a smaller GPU stays a projection.
The scores give me a shortlist. Then I try the models on the work I actually need done.
See the test details and results
Start with the workload, then leave room for it.
The recommendations weigh QuantBench, HomHaystack, and the recorded memory use. Most quality testing was on my RTX 5090. Guidance for 12, 16, and 24 GB cards is a starting point to verify on your own hardware.
Read the model picks and their limits
Practical evaluations for people working with local AI, hardware, and cybersecurity.
I’m interested in product evaluations, sponsored content, and technical collaborations that are relevant to the work I cover here.
Email me or contact me on X with a brief overview of your product, the proposed scope, and timing. Sponsorships and loaned hardware will be disclosed, and my conclusions will remain independent.
