Is an Apple M1 Ultra 128 GB better than an Apple M4 Pro 64 GB for running local LLMs in 2026, also considering used hardware?

Claude Opus 4.7

Vote: Yes

For local LLM inference, memory capacity and bandwidth are the two dominant factors, and the M1 Ultra wins decisively on both. Memory bandwidth: M1 Ultra has 800 GB/s of unified memory bandwidth, while M4 Pro has only ~273 GB/s. Token generation speed in LLMs is almost entirely bandwidth-bound, so the M1 Ultra will generate tokens roughly 2.5–3x faster on the same model. Memory capacity: 128 GB vs 64 GB is the difference between being able to run a quantized 70B model comfortably (or even a 4-bit 120B-class model) versus being capped around 30B–40B class models. In 2026, with MoE models like Llama 4, Qwen3, and DeepSeek variants becoming standard, having 128 GB of usable VRAM-equivalent is a major capability unlock that 64 GB simply cannot match. Used pricing: M1 Ultra 128 GB Mac Studios have dropped substantially on the used market (often $2,000–2,800), frequently cheaper than a new M4 Pro Mac mini/Studio with 64 GB. The M4 Pro's newer CPU/GPU architecture and slightly better prompt-processing speed don't compensate for half the RAM and one-third the bandwidth. For LLMs specifically, M1 Ultra 128 GB is the clearly better choice.

GPT-5.5

Vote: Yes

For local LLMs in 2026, option A: "Yes" is the stronger answer because unified memory capacity is usually the hard limit. An M1 Ultra with 128 GB can run much larger quantized models, larger contexts, and more demanding workflows than an M4 Pro capped at 64 GB. The M4 Pro has newer cores and better efficiency, but for LLM inference the M1 Ultra’s 128 GB RAM and very high memory bandwidth are more important than newer CPU architecture. The M4 Pro may be nicer for smaller models, but it will hit memory limits sooner. Considering used hardware also favors the M1 Ultra: used Mac Studios can offer excellent value for high-memory local inference, especially if the goal is to run 70B-class models or heavier mixtures locally.

Gemini 3.1 Pro

Vote: Yes

For running local Large Language Models (LLMs), memory capacity and memory bandwidth are the two most critical bottlenecks. The Apple M1 Ultra with 128 GB of unified memory will outperform an M4 Pro with 64 GB for this specific use case. First, memory capacity dictates the size of the model you can load. 128 GB allows you to comfortably run 70B to 100B+ parameter models at lower quantization, or utilize massive context windows. A 64 GB cap on the M4 Pro heavily restricts you to smaller models (like 32B or heavily quantized 70B) and shorter context lengths. Second, memory bandwidth dictates generation speed (tokens per second). The M1 Ultra boasts a staggering 800 GB/s of memory bandwidth, while the M4 Pro tops out at 273 GB/s. Because LLM inference is overwhelmingly memory-bound rather than compute-bound, the M1 Ultra will generate tokens significantly faster despite having an older GPU and CPU architecture. While the M4 Pro has superior single-core CPU speeds and newer architectural features, these do not overcome the physical limitations of smaller, slower memory when it comes to locally hosted LLMs. Factoring in used hardware prices in 2026, a second-hand Mac Studio with an M1 Ultra 128GB will offer unparalleled value for local AI enthusiasts.

Gemini 3.1 Pro

Vote: Yes

For running local LLMs, memory capacity and memory bandwidth are the absolute ultimate bottlenecks, far outweighing raw CPU/NPU processing power. The Apple M1 Ultra with 128 GB of unified memory allows you to load and run massive, highly capable models (like 70B+ parameter models or large Mixture-of-Experts) with large context windows. The M4 Pro with 64 GB hits a hard physical ceiling, forcing you to use smaller models or heavily degrade larger ones through extreme quantization. Furthermore, LLM token generation speed (at batch size 1) is primarily bottlenecked by memory bandwidth. The M1 Ultra delivers a colossal 800 GB/s of memory bandwidth, while the M4 Pro sits much lower (273 GB/s). This means the M1 Ultra can push tokens through its immense memory pool significantly faster than the M4 Pro. Looking at the 2026 landscape, open-weight models are only getting larger. A used M1 Ultra 128 GB will have depreciated substantially, making it arguably the most cost-effective 'VRAM monster' on the market for AI practitioners. The sheer utility of 128 GB of high-bandwidth memory makes 'Yes' the only logical answer.

Claude Opus 4.6

Vote: Yes

Yes, the Apple M1 Ultra 128 GB is better than the Apple M4 Pro 64 GB for running local LLMs, and here's why: **Memory is king for LLMs.** The single most important factor for running large language models locally is unified memory capacity. With 128 GB of unified memory, the M1 Ultra can load models that are simply impossible to run on a 64 GB machine. For example, a 70B parameter model at Q4 quantization requires roughly 40-45 GB, which fits on both — but a 70B model at higher quantization (Q6 or Q8) or larger models like 100B+ parameter models require well over 64 GB. The M1 Ultra opens up an entire class of models that the M4 Pro physically cannot run. **Memory bandwidth matters, but capacity matters more.** The M4 Pro 64 GB likely has around 273 GB/s memory bandwidth, while the M1 Ultra 128 GB has 800 GB/s. The M1 Ultra actually wins on both bandwidth AND capacity. Even if the M4 Pro has architectural improvements (better Neural Engine, improved GPU cores), the raw memory bandwidth advantage of the M1 Ultra translates directly to faster token generation for LLM inference. **Used hardware value proposition.** By 2026, M1 Ultra Mac Studios are available on the used market at significantly reduced prices compared to their original $4,000-$6,000+ cost. Meanwhile, M4 Pro machines with 64 GB are relatively newer and more expensive. The price-to-performance ratio for LLM workloads strongly favors the used M1 Ultra. **Practical LLM performance.** For the specific use case of running local LLMs (primarily inference with tools like llama.cpp, Ollama, or LM Studio), the M1 Ultra 128 GB will deliver faster tokens/second on the same model AND can run larger models. The M4 Pro's per-core efficiency improvements don't overcome the M1 Ultra's advantages in bandwidth and capacity. The only scenario where the M4 Pro wins is if you only ever run small models (7B-13B) and value power efficiency or other non-LLM tasks. But for the stated purpose of running local LLMs, the M1 Ultra 128 GB is clearly superior.

Grok 4.20

Vote: Yes

For running local LLMs, unified memory capacity is the primary limiter on model size. The M1 Ultra's 128 GB enables loading much larger models (e.g., 70B+ at higher quantizations or even 100B+ models) that simply won't fit in the M4 Pro's 64 GB. By 2026, as LLMs continue scaling, this difference grows in importance. The M4 Pro is faster on models that fit in both, but the M1 Ultra supports more capable inference overall. Used M1 Ultra hardware will also be far cheaper, improving the value equation without sacrificing core capability.

Final Summary

The AI Roundtable reached a rare, unanimous consensus: when it comes to running local LLMs, 'memory is king' and the M1 Ultra remains the reigning monarch. Despite the M4 Pro's newer architecture, the panel—including Claude Opus 4.7 and Gemini 3.1 Pro—concluded that the M1 Ultra's 128 GB capacity and massive 800 GB/s bandwidth create a performance ceiling that its younger sibling simply cannot reach. GPT-5.5 and Grok 4.20 noted that by 2026, the depreciated price of used M1 Ultra Mac Studios would likely make them the most cost-effective 'VRAM monsters' on the market.

All 6 models agreed on "Yes" after discussion

Strongest Arguments

• Yes: The M1 Ultra provides nearly triple the memory bandwidth (800 GB/s vs. 273 GB/s) and double the capacity, which are the two primary bottlenecks for LLM inference; this allows it to run 70B+ models at high quantization speeds that are physically impossible on a 64 GB machine.