Direct Answer: After Windows, macOS, or Linux reserves ~3 GB to 3.5 GB, 32 GB of RAM gives you ~28.5 GB to 29 GB of local LLM working memory. That comfortably runs 7B–8B models at lossless Q8_0 (64K+ context), 12B–14B models at Q6_K / Q8_0 (32K+ context), 26B–27B models at Q5_K_M, and near-frontier 30B MoE and 32B dense models at 4-bit Q4_K_M (~24.5 GB to 27.7 GB total working memory at 8K–16K context). However, speed depends on memory bandwidth: a single 32 GB CPU-only PC runs dense 32B models at 3–6 tokens/sec, whereas pooling a 12 GB RTX 3060 PC + 16 GB Apple Silicon Mac via the RAMDeck Hardware Hub ($199) streams 30B–32B class models at 28 to 39 tokens/sec!
Exact 32GB Local LLM Working Memory Breakdown (Running 32B Q4 on 32GB RAM)
- 32B
Q4_K_MGGUF Model Weights: ~19.8 GB (or ~18.0 GB for 30B MoE coding models). - Context Window KV Cache: ~2.2 GB at 8K tokens (~4.4 GB at 16K tokens; ~2.9 GB at 32K with Q8 KV quantization).
- OS + Runtime Graph Overhead: ~2.5 GB to 3.5 GB.
- Total Working Memory: ~24.5 GB to 27.7 GB—fitting cleanly inside 32 GB without touching slow SSD swap, while dense 70B
Q4_K_Mmodels (~45 GB+) require either 64 GB+ or adding a third 16 GB laptop to your RAMDeck pool (48 GB total).
Explore the full 8B–70B quantization table and live video benchmarks in 32GB RAM LLM Performance & How to Run a 32B LLM Locally and calculate your exact setup in the Interactive Local LLM RAM Calculator.
— The TradeMAV Team
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