Best LLM for Mac Studio M5 Ultra with 512GB RAM (2026)

The Mac Studio M5 Ultra with 512GB unified memory is the most capable local AI machine Apple has ever sold. Announced August 26, 2026, shipping late October, it delivers 1.2 TB/s memory bandwidth and roughly 435GB usable for models after macOS overhead. That loads Llama 4 Maverick 400B at Q4, runs multiple 120B-class MoE models simultaneously, and fits DeepSeek-R1 671B at a low quant. Starting at $5,499 for the base Ultra and up to $18,299 fully loaded, the 512GB tier is for buyers who already know why they need it: 400B-class MoE models, batch processing at scale, or multi-agent orchestration that would otherwise rent cloud GPU time. See our Mac Studio M5 Ultra flagship article for the full hardware breakdown and the upgrade comparison for bandwidth context.

TL;DR: The 512GB M5 Ultra is the no-compromise box. Our top pick is Llama 4 Maverick 400B at ~12 tok/s est. - a 400B MoE that is cloud-only on any other Mac config. For fast daily work, GPT-OSS 120B runs at ~43 tok/s est. with massive headroom, and DeepSeek-R1 671B loads at Q4 for ~6 tok/s est. batch reasoning. Cross-shop the 96GB and 256GB pages if you are not sure you need this much memory.

Estimated token generation on the Mac Studio M5 Ultra 512GB. ModelFit engine estimates.

How 512GB Shapes Your Model Choices

With ~435GB usable, the 512GB M5 Ultra opens model classes that were previously cloud-only. The headline is Llama 4 Maverick 400B at Q4 (~245GB), which loads with ~190GB of headroom for 128K+ token context sessions. Below that, every 120B-class MoE model fits with excellent margin, and you can load two or three simultaneously for multi-agent architectures.

The 512GB tier also enables DeepSeek-R1 671B at Q4_K_M (~380GB), but it is a tight fit. With ~55GB remaining for KV-cache, context is limited to roughly 16K-32K tokens. Speed is slow too: ~6 tok/s est., since 37B active parameters need to stream through bandwidth. This model is practical for overnight batch processing, not interactive chat.

Batch speed advantages compound at this tier. The 50% bandwidth uplift over M3 Ultra means that even for large models running at a few tok/s, total throughput over a multi-hour batch job is significantly higher.

Best Models Ranked

Model Details

Llama 4 Maverick 400B is the reason to buy 512GB. At Q4_K_M it loads ~245GB, leaving nearly 200GB for context and other models. Its 17B active parameters generate at ~12 tok/s est. - slow for rapid chat but transformative for batch work, code review over large repos, and overnight agent tasks that never touch an API. This is the first Mac that runs a 400B-class model entirely in memory.

ollama run llama4:maverick

GPT-OSS 120B is the best daily driver on this config. At ~65GB in MXFP4, it loads with 370GB of headroom - you can run it alongside Llama 4 Maverick and still have room for a third model. Its 5.1B active parameters deliver ~43 tok/s est., making it the fastest serious reasoning model here.

ollama run gpt-oss:120b

Laguna S 2.1 (118B, MoE, 8B active) loads at ~96GB in Q4_K_M, fitting easily alongside any model on this list. Its focus on agentic coding and long-horizon task execution makes it a strong secondary model for specialized agent pipelines. Expect ~32 tok/s est.

ollama run laguna-s-2.1:q4_K_M

Qwen3.5 122B-A10B loads at ~72GB in Q4_K_M, the lightest of the 120B-class options. Its 10B active parameters produce frontier reasoning at ~28 tok/s est., and the massive leftover capacity means you can run it with multiple companions.

ollama run qwen3.5:122b-a10b

DeepSeek-R1 671B at Q4_K_M (~380GB) is the stretch model. Its 37B active parameters make it the most compute-heavy model here at ~6 tok/s est. It loads, but context is limited and speed is low. Use it for overnight batch coding analysis or document processing where turnaround time does not matter. Estimates are provisional until shipped units run benchmarks.

ollama run deepseek-r1:671b-q4_K_M

Llama 4 Scout (109B, MoE, 17B active) loads at ~67GB in Q4_K_M, a trivial fit. Its strength is long-context multimodal work: the 512GB tier gives it hundreds of GB of KV-cache headroom for million-token context experiments.

ollama run llama4:scout

What Does Not Fit or Runs Too Slow

Even on 512GB, some models are impractical. Llama 3.1 405B dense at Q4 (~243GB) loads but offers no advantage over Llama 4 Maverick's MoE architecture. A full 405B-class dense at Q8 would need ~450GB, exceeding capacity.

DeepSeek-R1 671B fits but runs too slowly for interactive use at ~6 tok/s est. If your work requires DeepSeek-class models at chat speed, cloud GPU rental remains the correct answer. The 512GB M5 Ultra handles it for batch and overnight work, which is a genuine step forward, but expectations should match the hardware.

For everything from 7B through 120B MoE, this machine is overwhelmingly capable. The question is not whether a model fits, but how many you can load at once.

One release-day note (August 26): Qwen3.8-Flash-Next, the open-weight Qwen4 preview with a 51B n-gram table, fits here with an Excellent rating at ~32 tok/s est. Its ~123GB 1-bit build is exactly the kind of model 512GB is bought for. Details in our launch coverage.

FAQ

Can the 512GB M5 Ultra run Llama 4 Maverick?

Yes. Llama 4 Maverick 400B at Q4_K_M loads in ~245GB, fitting comfortably with ~190GB headroom for KV-cache and context. Expect ~12 tok/s est. This is the first Mac config where Maverick runs entirely in memory.

M5 Ultra or cloud GPU for large models?

For 400B-class models at batch speed, the 512GB M5 Ultra is cost-competitive with monthly cloud GPU rental within 6-12 months depending on usage. For interactive-speed work on 671B-class models, cloud GPUs remain faster. The M5 Ultra wins on privacy, offline availability, and zero per-token cost.

Is 512GB overkill for most users?

Yes. If your daily models are in the 7-70B range, the 96GB or 256GB M5 Ultra is the better buy. The 512GB tier is for people who run 400B-class MoE models, batch process at scale, or run multi-model agent architectures that need several large models loaded simultaneously.

What is the best batch workload for the 512GB M5 Ultra?

Llama 4 Maverick loaded with 128K context and running overnight code reviews or document analysis. The ~12 tok/s est. translates to roughly 43K tokens per hour, which covers a substantial codebase in a single session. For maximum throughput per hour, run GPT-OSS 120B instead at ~43 tok/s est. for 155K tokens per hour.

When does the 512GB M5 Ultra ship?

Pre-orders opened August 26, 2026, but the 512GB configuration ships in late October 2026, about a month after the 96GB and 256GB tiers. The lower-RAM configs ship on September 22 as standard.

Can you run DeepSeek-R1 671B on the 512GB M5 Ultra?

Yes, at Q4_K_M (~380GB) it loads in 512GB. But it is a tight fit with limited context headroom, and at ~6 tok/s est. it is not interactive. It is practical for overnight batch tasks only. For the 256GB and 96GB tiers, DeepSeek does not fit at any usable quantization.