AI Models
AI models predict based on their training data. They can work in any domain such as numbers, text or multimedia.
LLM Latency Benchmark by Use Cases
We benchmarked 11 top large language models with a total of 1,320 requests, splitting reasoning and non-reasoning models, and measured first-token latency, per-token latency, and overall response time. You can find details on how we measured latency here. We report reasoning and non-reasoning models separately. Reasoning models spend several seconds thinking before the first visible…
Tabular Models Benchmark: Performance Across 19 Datasets
We benchmarked 8 tabular learning models on 19 real-world datasets covering roughly 260,000 samples, with dataset sizes from 435 to 48,800 rows. Every model ran on the same machine with 5-fold cross-validation and identical splits. Each dataset is a round-robin of head-to-head matches between models, decided by the primary metric. Elo aggregates all 483 matches…
LLM Quantization: BF16 vs FP8 vs INT4
We benchmarked Qwen3-32B at 4 precision levels (BF16, FP8, GPTQ-Int8, GPTQ-Int4) on a single NVIDIA H100 80GB GPU. Each configuration was evaluated on 2 benchmarks (~12.2K questions) covering knowledge and code generation, plus 2,000+ inference runs to measure throughput. Int4 is 2.7x faster than BF16 while losing less than 2 points on MMLU-Pro, but code…