M4 Max vs M5 Max for local LLMs: The M4 Max offers superior value

This review evaluates two MacBook Pro configurations, M4 Max and M5 Max, for running local large language models like Gemma 4 31B and Qwen3.6-27B Q8, focusing on performance implications and cost efficiency.

TL;DR Best for: Data scientists and intelligence analysts needing substantial local LLM capacity without overspending on marginal performance gains. Skip if: Your workflow demands absolute peak pre-fill performance and budget is not a primary constraint. Bottom line: The refurbished M4 Max MacBook Pro offers a significantly better performance-to-price ratio for the specified local LLM workloads.

METHODOLOGY

This v0 review draws on the user 'roguefunction's published claims and specifications on Reddit, accessed on 2026-05-28. Independent benchmarks are pending. Update cadence: re-tested when claims diverge from observed behavior. This analysis covers the theoretical performance implications of the specified hardware configurations for local LLM inference, specifically focusing on the Apple M4 Max and M5 Max chips. We evaluate the stated CPU, GPU, RAM, storage, and particularly the memory bandwidth figures provided by the user. What is covered includes the potential impact of these specifications on running models like Gemma 4 31B Q8 and Qwen3.6-27B Q8. What is not covered includes independent performance benchmarks, real-world thermal throttling under sustained load, long-term workflow integration, or edge-case inference scenarios. Our assessment is based on the provided technical details and the user's stated use case.

WHAT IT DOES

M4 Max configuration

The refurbished 16-inch MacBook Pro features an Apple M4 Max Chip with a 16‑core CPU and 40‑core GPU. It includes 64GB of unified RAM and a 1TB SSD. The memory bandwidth for the 40-core GPU M4 Max is specified at 546 GB/s. This configuration is priced at $3,479.00.

M5 Max configuration

The new 16-inch MacBook Pro is equipped with an Apple M5 Max Chip, featuring an 18‑core CPU and a 40‑core GPU. It also comes with 64GB of unified RAM, but offers a larger 2TB SSD. The memory bandwidth for the 40-core GPU M5 Max is listed at 614 GB/s, representing a 12.5% increase over the M4 Max. This configuration is priced at $4,599.00.

LLM workload specifics

The user, a data scientist and intelligence analyst, intends to run local LLMs such as Gemma 4 31B at Q8 quantization and Qwen3.6-27B Q8. The primary tasks involve data derivation and data element extraction. The user is currently constrained by a 24GB shared RAM ceiling, making the 64GB RAM in both proposed configurations a significant upgrade for these models.

WHAT'S INTERESTING / WHAT'S NOT

What's interesting in this comparison is the substantial upgrade in unified memory from the user's current 24GB to 64GB in both potential machines. This memory increase is the primary enabler for running the specified 31B and 27B parameter models at Q8 quantization locally. The M5 Max's 12.5% increase in memory bandwidth (from 546 GB/s to 614 GB/s) is a concrete, quantifiable improvement that directly impacts LLM performance, particularly during the pre-fill phase where large contexts are processed. The $1,120 price difference between the two configurations is also a significant factor, making the M4 Max a compelling value proposition if the bandwidth gain does not translate to a proportional real-world performance increase for the user's specific models and tasks.

What's not particularly interesting or verifiable without independent testing are claims like

Pull quote: “The refurbished 16-inch MacBook Pro features an Apple M4 Max Chip with a 16‑core CPU and 40‑core GPU.”