Lokale Inferenz
Definition
Lokale Inferenz bedeutet das Ausführen von LLMs, vision, or other Modellen auf eigener Hardware—a laptop, workstation, on-prem server, or edge device—anstatt calling a cloud API. Data never leaves your environment, was unterstützt Datenschutz, latency, cost control, and offline use.
Es basiert auf model compression (quantization, pruning, knowledge distillation) and efficient runtimes so models fit in limited memory and run without a GPU or with consumer GPUs. Tools like Ollama, LM Studio, llama.cpp, vLLM, and TensorFlow Lite enable local inference mit minimalem setup.
Funktionsweise
You obtain model weights (z. B. GGUF, SafeTensors) aus dem Hub or a vendor. A runtime (Ollama, llama.cpp, vLLM, TFLite) loads the model onto CPU, GPU, or NPU and executes the forward pass. Quantization (INT8, INT4, GPTQ, AWQ) shrinks memory so larger models fit; batching and KV cache improve throughput when serving multiple requests. No network call to a cloud API—inference runs entirely auf dem local machine or cluster.
Anwendungsfälle
Local inference passt, wenn Datenschutz, latency, cost, or offline operation matters more than using the largest cloud model.
- Privacy-sensitive or regulated data (healthcare, legal, internal docs) that must not leave the network
- Low-latency or real-time apps (IDE, assistants) where round-trips to the cloud are unacceptable
- Cost control at scale or air-gapped / offline environments
- Development and testing without API keys or usage limits
Vor- und Nachteile
| Pros | Cons |
|---|---|
| Data stays on your infrastructure | Smaller or quantized models; possible quality drop |
| No per-token API cost zur Inferenzzeit | You own hardware and ops (GPU, memory, updates) |
| Works offline and in restricted networks | Throughput and context length limited by hardware |
| Full control over model version and behavior | Need quantization and compression for larger models |
Externe Dokumentation
- Ollama — Run LLMs locally with a simple API
- llama.cpp — C++ inference for LLaMA and compatible models
- vLLM — High-throughput server for local or on-prem LLM serving
- TensorFlow Lite — On-device inference for mobile and edge