Benchmarks Guides
9 InsiderLLM guides in Benchmarks — practical, tested walkthroughs for running AI locally, sorted by most recently updated.
- I Got the AI Result I Wanted. Then I Ran It Nine More Times A frontier model read my logs and wrote a skill that beat my local 27B baseline by 10.6 points. Nine more compilation runs showed the number was fake.
- Qwen 3.8 27B vs 3.6 on RTX 3090: Speed and Quality Tested Firsthand benchmarks of Qwen 3.8-27B against 3.6-27B on one RTX 3090: generation within a percent, VRAM +254 MiB, and HumanEval pass@1 a statistical tie.
- Ornith 1.5 35B vs Qwen 3.6 on RTX 3090: Speed Tested Firsthand A-B-B-A bench of Ornith 1.5-35B-A3B against Qwen 3.6-35B-A3B on one RTX 3090. Generation, prefill, VRAM, and the noise floor under all three.
- Why Qwen 3.8 27B Feels Slow: Reasoning Tokens Measured Qwen 3.8 27B generates at full speed on a 3090 and still crawls. Four runs, two models, two seeds: 92.8% of output is thinking, and the spread runs 320x to 542x.
- How to Get 2.5x Faster Qwen on RTX 3090 (Free) I built DFlash on my RTX 3090 and ran the full bench. Real 2.5x speedup on Qwen 3.5 and 3.6 — below the 3.43x README claim, still huge. Here's how.
- DFlash vs MTP on RTX 3090: I Tested Both Locally Firsthand head-to-head bench of DFlash + DDTree against MTP (PR #22673) on a single RTX 3090, same Qwen 3.6-27B target. Real numbers, both backends.
- Wicked Fast Qwen 3.6 27B: 60 tok/s with MTP on RTX 3090 (2026) Firsthand bench: 60 tok/s on Qwen 3.6 27B Q4_K_M with MTP on a single RTX 3090 — 1.86x wall-clock speedup over baseline. PR #22673 progress May 6 → May 19.
- Best 24GB Backend Shootout: ik_llama vs BeeLlama vs llama.cpp ik_llama and BeeLlama both finish in 22-23s on the am17an 9-prompt harness vs mainline llama.cpp's 37s — 1.66x and 1.62x speedups via opposite strategies.
- Wicked Fast Gemma 4 vs Qwen 3.6 on RTX 3090: 3.10x Tested Same RTX 3090, same llama.cpp build, same bench. Gemma 4 26B-A4B Q4_K_XL: 128 tok/s mean. Qwen 3.6-27B Q4_K_M: 41 tok/s. 3.10x faster, firsthand.