LLM Anwendungsfälle, Analysen & Benchmarks
LLMs sind KI-Systeme, die anhand umfangreicher Textdaten trainiert werden, um menschliche Sprache für Geschäftsprozesse zu verstehen, zu generieren und zu verarbeiten. Wir vergleichen Leistung, Anwendungsfälle, Kosten, Bereitstellungsoptionen und Best Practices, um die Einführung von LLMs in Unternehmen zu unterstützen.
LLM Anwendungsfälle, Analysen & Benchmarks erkunden
LLM Marktanteil: Nutzung & Adoption vergleichen
We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: LLM market share comparison by country Read the methodology to see how we measured and calculated these results.
Text-to-SQL: Vergleich der LLM-Genauigkeit
I have relied on SQL for data analysis for 18 years, beginning in my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases.
LLM Quantisierung: 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.
10+ Beispiele für große Sprachmodelle & Benchmark
We have used open-source benchmarks to compare top proprietary and open-source large language model examples. You can choose your use case to find the right model. Comparison of the most popular large language models We have developed a model scoring system based on three key metrics: user preference, coding, and reliability.
LLM in der Cybersicherheit
We evaluated 7 large language models across 9 cybersecurity domains using SecBench, a large-scale and multi-format benchmark for security tasks. We tested each model on 44,823 multiple-choice questions (MCQs) and 3,087 short-answer questions (SAQs), covering areas such as data security, identity & access management, network security, vulnerability management, and cloud security.
LLM Observability-Tools: Weights & Biases, Langsmith
LLM-based applications are becoming more capable and increasingly complex, making their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected. LLM observability addresses this challenge by providing continuous visibility into how models operate in real-world conditions.
LLM Parameter: GPT-5 High, Medium, Low und Minimal
New LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. Price vs.
LLM Latenz-Benchmark nach Anwendungsfällen
The effectiveness of large language models (LLMs) is determined not only by their accuracy and capabilities but also by the speed at which they engage with users. We benchmarked the performance of leading language models across various use cases, measuring their response times to user input.