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MacBook Pro M5 Max performance questioned for local LLM coding

A user on Reddit is inquiring about the practical performance of a 128 GB MacBook Pro M5 Max for local large-context LLM coding workflows. They are specifically concerned with prompt ingestion and prefill latency, rather than raw token generation speed. The user is interested in using models like Qwen 3.5-3.7 for coding tasks on large codebases and wants to understand performance metrics such as prompt processing speed, time-to-first-token (TTFT), and how performance degrades with context window size. AI

IMPACT Assesses the practical limitations of high-end consumer hardware for demanding local LLM applications.

RANK_REASON User inquiry about hardware performance for a specific AI task.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/bajis12870 ·

    Is a 128 GB MacBook Pro M5 Max actually too slow for large-context local LLM coding workflows?

    <!-- SC_OFF --><div class="md"><p>People are warning me about the <strong>prompt-processing</strong> speed of a <strong>MacBook Pro M5 Max</strong> with <strong>128 GB</strong> RAM.</p> <p>My main concern is <strong>prompt ingestion</strong> / <strong>prefill latency</strong> and…