Discover how to calculate memory requirements for local LLMs and run language models on even low-end PCs. We'll cover all factors that affect RAM usage – model size, KV cache, precision, batch size, etc. – and show how to compute model weight and cache usage. Along the way, we'll share practical examples (Ollama, LM Studio, Kolosal.ai) and give optimization tips for CPU-only LLM deployment. By the end, you'll know exactly how much RAM you need to run different LLMs locally, and how to optimize for your hardware.
Factors Affecting Memory Requirements
- Model size (parameters and precision): Model weights dominate memory usage. For example, 7B FP16 = ~14 GB. Lower precision (INT8) cuts this in half.
- KV cache: Grows with context length and layers. A 4096-token context on 7B uses ~2 GB.
- Batch size: Higher batch = more memory. Stick to batch=1 for minimal RAM use.
- Other factors: Concurrency, beam search, and training states add memory needs.
How to Calculate Memory Usage
Model weight (in GB) ≈ parameter count × bytes per parameter. E.g., 7B FP16 = 7e9 × 2 bytes = ~14 GB. Add ~10–20% overhead. KV cache ≈ batch × seq_len × layers × heads × head_dim × 2 × bytes. Llama 7B (4096 tokens) needs ~2 GB cache.
Practical Examples
- Ollama: 7B model = ~16–32 GB RAM. 13B = 32–64 GB.
- LM Studio: Recommends 16 GB+ for most models.
- Kolosal.ai: Optimized for 1–3B on ~8–16 GB. Ideal for low-end PCs.
Typical Memory Needs by Model Size
| Model Size | Weights | KV Cache | Total RAM |
|---|---|---|---|
| 7B | ~14 GB | ~1 GB | 16–32 GB |
| 13B | ~26 GB | ~1.6 GB | 32–64 GB |
| 33B | ~66 GB | ~3 GB | 80+ GB |
Tips for CPU-Only Optimization
- Quantize weights: Use INT8 or INT4 to cut memory use by 2–4×.
- Reduce context: Halve token count to halve KV memory.
- Keep batch size = 1: Simplest way to reduce usage.
- Efficient attention: Use FlashAttention or Grouped Attention.
- Smaller models: Pick 1–3B if limited RAM.
- Disk offload: Memory map weights to save RAM (slower).
- Use good tools: Kolosal.ai, Llama.cpp, Ollama automate optimizations.
FAQ
- How much RAM for 7B? 16–32 GB recommended.
- What is KV cache? Stores key/value vectors for fast generation. Grows with context.
- Can I run LLMs without GPU? Yes, CPU works fine with enough RAM. Use quantized models.
- How does sequence length affect RAM? Linearly. Longer input = more KV cache needed.
- Tools to estimate RAM? Hugging Face Accelerate, Kolosal.ai calculators, or param × bytes estimate.
Estimating RAM for local LLMs is key for smooth performance. Sum model weight + KV cache to plan your setup. Use smaller or quantized models for low-end PCs. Try Kolosal.ai for an easy, optimized way to run LLMs locally.