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Running Local LLMs on Your PC: What You Need to Know

1/30/2026 · AI · 7 min

Running Local LLMs on Your PC: What You Need to Know

TL;DR

  • Running local LLMs can improve privacy and latency but requires significant storage and RAM.
  • For compact models (7B), expect to need 8-16 GB VRAM or 16-32 GB system RAM depending on quantization and tooling.
  • Larger models (13B+) often need 24+ GB VRAM or rely on CPU offloading, quantization, or model sharding.
  • Best setups by use case:
  • Casual experimentation: CPU + 16+ GB RAM with small quantized models.
  • Offline assistant: 8-bit quantized 7B on a 6-8 GB GPU or 32 GB RAM CPU fallback.
  • Heavy inference/productivity: 24+ GB GPU (e.g., RTX 4090 class) with room for 13B+ models.

Why Run Locally?

  • Privacy: Data stays on your machine rather than a cloud API.
  • Latency: Faster prompt-to-response times without network round trips.
  • Cost: No per-request billing; one-time hardware and electricity costs instead.

Model Sizes and What They Mean

  • 3B to 7B: Lightweight, good for experimentation and low-latency tasks.
  • 13B: Solid balance between capability and resource needs; better at reasoning and instruction-following.
  • 30B+: Higher accuracy and understanding but require serious resources.

RAM, VRAM, and Storage Requirements

  • VRAM matters for GPU inference. Example targets:
  • 7B quantized: 6-8 GB VRAM.
  • 13B quantized: 12-16 GB VRAM.
  • 30B+: 24+ GB VRAM or model parallelism.
  • System RAM: Keep at least 1.5x model size for comfortable swapping and tooling.
  • Storage: Models range from a few gigabytes (quantized) to hundreds of gigabytes for full precision checkpoints. Use NVMe for faster load times.

Quantization and Acceleration

  • Quantization reduces precision to cut memory by 2x-4x with limited accuracy loss.
  • Tools like GGML, bitsandbytes, and ONNX runtime enable CPU and GPU acceleration.
  • CPU inference works for small quantized models but expect slower token rates.

Performance Tips

  • Use batch tokenization sparingly for single-user interactive use.
  • Keep models cached on NVMe to avoid repeated loading delays.
  • Prefer models optimized for latency when building assistants.

Privacy and Security Trade-offs

  • Local models improve privacy but still need secure storage and OS updates.
  • Avoid running untrusted model binaries; prefer verified repositories or build from source.

Which Setup Should You Choose?

  • Choose CPU only if you need full offline capability and run tiny quantized models.
  • Choose a GPU if you want low latency and higher quality models: 8 GB for small models, 24+ GB for larger models.
  • Consider hybrid setups: local GPU for inference and optional cloud for heavy tasks.

Buying Checklist

  • GPU VRAM target based on model size.
  • At least 32 GB NVMe for models and swap space.
  • 16-64 GB system RAM depending on workload.
  • Cooling and power: inference can be sustained and hot.

Bottom Line

Running LLMs locally is increasingly practical for personal use and privacy-focused workflows. Start with small quantized models to learn the tools, then scale hardware as your needs grow. Local inference gives control and low latency, but be realistic about the hardware needed for higher-quality models.

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