# Calculating Local LLM Performance on Apple Silicon Hardware A practical guide to matching model parameter counts with unified memory and bandwidth. - **Source:** [Hacker News (news.ycombinator.com)](https://brifing.si/source/13c923cd-6d99-442b-9b8b-1181f4d8cd91) - **Published:** 2026-10-17 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/808dba19-dcbb-4f62-b7c5-0bc856c85cdc ## Unified Memory Constraints Running LLMs locally on Apple Silicon depends on two primary factors: fitting the model into unified memory and the system's memory bandwidth. Parameter counts determine the memory footprint, while bandwidth dictates the token generation speed. Users must account for macOS overhead and KV cache requirements to prevent performance-killing SSD swapping during inference. ## The Math of Model Fitting To calculate memory needs, multiply billions of parameters by bits per weight, then divide by eight. For a Llama 3.3 70B model at Q4_K_M quantization, you need roughly 43 GB for weights, plus 15 percent runtime overhead and context cache. Always reserve 3 GB for macOS to ensure system stability during heavy tasks. ## Bandwidth Defines Generation Speed Generation speed is strictly limited by memory bandwidth because every active weight must be read from memory for each token produced. Increasing GPU cores does not improve generation speed if the memory bus remains the same. A 24 GB M4 and a 16 GB M4 perform identically on generation tasks due to bandwidth limits. ## Fixed Costs Per Token Most performance calculators ignore fixed costs like sampling and kernel launches, leading to inaccurate predictions for smaller models. By incorporating a 3.3 ms overhead constant, this model predicts 177 tokens per second for a 3B model, whereas bandwidth-only models incorrectly estimate 525. This ensures accuracy even when models are small. ## MoE Model Performance Dynamics Mixture of Experts models require enough unified memory to hold all experts, regardless of how many are active. However, generation speed is determined only by the active parameter count. This allows a 671B MoE model with 37B active parameters to generate tokens faster than a dense 70B model despite the massive memory requirement. ## Benchmark Accuracy and Validation The methodology relies on ten dense runs across two machines, covering bandwidths from 120 GB/s to 307 GB/s. These benchmarks reproduce measurements within 4.6 percent accuracy. By fitting constants against these diverse hardware profiles, the calculator provides reliable planning numbers for hardware selection without requiring exhaustive testing on every specific machine configuration. ## Strategic Hardware Rental Options For users needing 64 GB of memory or more, renting dedicated Apple Silicon is a viable alternative to purchasing. A M4 Pro Mac mini with 64 GB costs 286 dollars monthly. This allows professionals to test specific model performance on their own documents before committing to a significant capital expenditure for high-end hardware. ## Avoiding Performance Bottlenecks A model that fits with zero spare memory will inevitably trigger SSD swapping when processing long documents. Swapping costs significantly more in performance than any quantization savings provide. Always prioritize a buffer in your memory configuration to maintain consistent token generation speeds and avoid the severe latency penalties associated with disk-based memory paging. ## Sources covering this story - https://macyou.co/mac-llm-calculator — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._