LLM Benchmarking

Head to head model runs on local hardware, architecture teardowns read straight from the weights, inference tuning, and the test bench every number comes from.

LLM Benchmarking Tutorials (12)

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Qwen 3.8 Flash Next (Qwen 4) vs Qwen 3.8 27B Architecture Teardown
Aug 27, 202621 min readArchitecture Teardowns

Qwen 3.8 Flash Next (Qwen 4) vs Qwen 3.8 27B Architecture Teardown

We read the config files and weight maps of Qwen3.8-Flash-Next and Qwen 3.8 27B to find the four changes that let a 180B model run only 6B of its weights per token.

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Granite 4.2 vs Gemma 4 vs Qwen 3.8 Benchmark
Aug 26, 202621 min readModel Comparisons

Granite 4.2 vs Gemma 4 vs Qwen 3.8 Benchmark

IBM dropped the Mamba hybrid in Granite 4.2. We measured what that costs against Gemma 4, Qwen 3.8 and Ornith 1.5 on one RTX 5090.

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Ornith 1.5 9B and 35B-A3B Benchmark on RTX 5090
Aug 25, 202619 min readModel Comparisons

Ornith 1.5 9B and 35B-A3B Benchmark on RTX 5090

We benchmarked Ornith 1.5 9B and 35B-A3B against Gemma 4-26B-A4B and Qwen 3.8 27B on one RTX 5090, and found the two mixtures are nearly identical on hardware and separated entirely by training.

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Qwen 3.8 27B Uncensored vs Base Abliteration Teardown
Aug 25, 202617 min readArchitecture Teardowns

Qwen 3.8 27B Uncensored vs Base Abliteration Teardown

We diffed all 866 GGUF tensors of an uncensored Qwen 3.8 27B against the original and found 131 matrices edited along one shared direction, with no measurable cost to quality.

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Qwen 3.8 27B vs Qwen 3.6 27B vs Qwen 3.5 27B Teardown
Aug 19, 202619 min readArchitecture Teardowns

Qwen 3.8 27B vs Qwen 3.6 27B vs Qwen 3.5 27B Teardown

We read the GGUF headers of Qwen 3.8 27B, Qwen 3.6 27B, and Qwen 3.5 27B and ran 397 measured generations on one RTX 5090 to find where the gains between the three releases really come from.

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Qwen 3.8 27B vs Muse Glimmer 30B vs Gemma 4 26B Consistency Test
Aug 19, 202625 min readModel Comparisons

Qwen 3.8 27B vs Muse Glimmer 30B vs Gemma 4 26B Consistency Test

We asked Qwen 3.8 27B, Muse Glimmer 30B, and Gemma 4 26B the same twelve questions ten times each on one RTX 5090 to measure which local model gives the same answer twice.

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Qwen 3.8 27B Speed Settings on llama.cpp
Aug 16, 202622 min readInference Tuning

Qwen 3.8 27B Speed Settings on llama.cpp

We ran 45 llama.cpp configurations of Qwen 3.8 27B on one RTX 5090 to find which settings make it faster: draft depth, KV cache type, context size, and reasoning effort.

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Qwen 3.8 27B vs Nemotron 3.5 vs Muse Glimmer on an RTX 5090
Aug 15, 202613 min readModel Comparisons

Qwen 3.8 27B vs Nemotron 3.5 vs Muse Glimmer on an RTX 5090

We ran Qwen 3.8 27B, Nemotron 3.5 Lightning, and Muse Glimmer on sixteen hard problems on one RTX 5090 to compare accuracy, speed, VRAM, and how each model fails.

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Grok 4.6 vs Gemini 3.7 Flash on Benchmarks and Pricing
Aug 14, 202610 min readModel Comparisons

Grok 4.6 vs Gemini 3.7 Flash on Benchmarks and Pricing

Grok 4.6 vs Gemini 3.7 Flash compared on the four benchmarks both companies actually publish, plus pricing, the FrontierCode label trap, and which model fits which workload.

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Nemotron 3.5 vs Muse Glimmer 30B on an RTX 5090
Aug 12, 20268 min readModel Comparisons

Nemotron 3.5 vs Muse Glimmer 30B on an RTX 5090

Hands-on benchmark of NVIDIA Nemotron 3.5 Lightning vs Meta Muse Glimmer 30B on one RTX 5090 with Ollama: generation speed, VRAM use, and long-context behavior.

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Advanced LLM Benchmark Prompt Suite
May 19, 202612 min readTest Bench

Advanced LLM Benchmark Prompt Suite

A comprehensive guide on progressive benchmarking for Large Language Models using structured prompt architectures and high-quality browser-based tests.

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Local LLMs 2026: Technical Reference
Mar 28, 202611 min readTest Bench

Local LLMs 2026: Technical Reference

A detailed technical guide comparing dense, sparse MoE, and hybrid SSM local LLM architectures for optimal deployment on 24GB VRAM hardware systems in 2026.

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