Local LLMs in 2025: Infrastructure and Performance
A comprehensive guide to running Large Language Models locally. We analyze hardware requirements, quantization techniques, and inference engines.
Runing Large Language Models (LLMs) locally has shifted from a novelty to a practical deployment strategy. It offers data privacy, zero latency networking, and operational cost stability. This guide analyzes the hardware and software stack required for effective local inference.
The Case for Local Inference
- Data Sovereignty: Highly sensitive data (legal, medical, proprietary code) never leaves the premises.
- Cost Predictability: High-volume token generation is limited only by electricity costs, avoiding API usage fees.
- Latency: Eliminating network round-trips enables real-time responsiveness for edge applications.
Hardware Requirements
The primary constraint for local inference is VRAM (Video RAM). To run a model, its weights must fit entirely into GPU memory for optimal performance.
Model Sizing & Quantization
Models are typically distributed in 16-bit precision (FP16). Quantization reduces this precision to 4-bit (or even lower) with minimal quality loss, significantly reducing VRAM usage.
| Model Size | Quantization | Required VRAM | Example GPU |
|---|---|---|---|
| 7B | 4-bit (Q4_K_M) | ~6 GB | RTX 3060 (12GB) |
| 13B | 4-bit (Q4_K_M) | ~10 GB | RTX 4070 (12GB) |
| 34B/30B | 4-bit (Q4_K_M) | ~20 GB | RTX 3090/4090 (24GB) |
| 70B | 4-bit (Q4_K_M) | ~40 GB | 2x RTX 3090 or Mac Studio |
Note: System RAM is useful for CPU offloading, but inference speed drops by an order of magnitude compared to pure GPU execution.
Inference Frameworks
1. llama.cpp
The de facto standard for efficient inference. It enables LLM execution on consumer CPUs and supports Apple Metal (MPS) and CUDA. It uses the GGUF file format.
2. Ollama
A wrapper around llama.cpp that provides a Docker-like experience for managing models. It exposes a local API server compatible with OpenAI’s format.
ollama run llama33. vLLM / TGI
For production deployments requiring high throughput, frameworks like vLLM (Virtual Large Language Model) utilize PagedAttention to manage memory efficiently, allowing for concurrent request batching.
Top Models for Local Use
- Llama 3 (8B/70B): The current open-weights state of the art.
- Mistral / Mixtral: Efficient architectures (including Mixture of Experts) that punch above their weight class.
- Phi-3: Microsoft’s small language model (SLM) capable of running on mobile devices.
Conclusion
The gap between proprietary APIs and local models is narrowing. With a modern consumer GPU and 4-bit quantization, developers can run intelligence capabilities locally that were previously restricted to data centers.