Best LLM for Mac Studio M5 Ultra with 96GB RAM (2026)

Apple announced the Mac Studio M5 Ultra on August 26, 2026, and the 96GB memory tier is where the Ultra line makes the most sense for most buyers. Starting at $5,499 with 1.2 TB/s of memory bandwidth, roughly 50% more than the M3 Ultra, this config gives you about 77GB usable for models after macOS overhead. That is enough to run 70B Q4 dense models with generous context and to load 120B-class MoE models at MXFP4 quantization. It is the practical Ultra: serious capacity without the top-of-the-line price. This guide ranks the models that make the most of 96GB on the M5 Ultra, with speed estimates from the ModelFit engine.

TL;DR: The 96GB M5 Ultra is the sweet spot for 70B-class work and entry-level 120B MoE. Our top pick is Kolibri 1, Aleph Alpha's German and English reasoning MoE, at Q4_K_M and ~181 tok/s est. (llama.cpp only, no Ollama tag). Next comes Qwen3.6 35B-A3B at Q8_0, a fast daily driver at near-lossless precision running at ~160 tok/s est. For long context at 80B scale, Qwen3-Next 80B-A3B runs at ~201 tok/s est. and loads at Q4 with room to spare. Nemotron 3 Super 120B-A12B fits in its IQ4_XS build at ~79 tok/s est. (llama.cpp only), and for raw reasoning GPT-OSS 120B loads at MXFP4 at ~146 tok/s est. See our Mac Studio M5 Ultra flagship article for full hardware details and see the upgrade comparison for bandwidth differences.

Estimated token generation on the Mac Studio M5 Ultra 96GB. ModelFit engine estimates.

How 96GB Shapes Your Model Choices

After macOS and system services take their share, the 96GB M5 Ultra leaves about 77GB for model weights and KV-cache. At Q4_K_M quantization, each billion parameters costs roughly 0.6GB. A 70B dense model needs about 42GB for weights, leaving 35GB for KV-cache - enough for context windows well beyond 128K tokens.

MoE models change the equation. A 120B MoE like GPT-OSS stores 117B parameters in memory but activates only ~5B per token. Memory tracks the total (it all has to fit), while speed tracks the active set. That is why the 96GB Ultra can load a 120B-class MoE at ~146 tok/s est. while a dense 70B would run at only ~22 tok/s est.

The 96GB tier is the first point where 120B-class MoE models load entirely in memory. They are tight fits (65-72GB for the largest), so long-context sessions need careful budgeting, but they load. The practical daily workstation range is 35-80B MoE, where you have margin for context and background apps.

Best Models Ranked

Speeds are ModelFit estimates with a typical error of about 10%.

Model Details

Kolibri 1 is Aleph Alpha's German and English reasoning MoE: 78B parameters stored with 3.46B active per token, Apache 2.0, and a native context of 262,144 tokens (model card). The card reports 84.3 on GPQA Diamond (EN) and 85.9 on LiveCodeBench v6, both publisher-reported. The bartowski Q4_K_M GGUF is 49.76GB (GGUF page), so it loads with roughly 27GB to spare for context and runs ~181 tok/s est. Ollama has no tag for it, so run it through llama.cpp, which added support in PR 30673. The GGUF was made with release b11716, so use that build or newer:

llama-server -hf bartowski/Kolibri-1-GGUF:Q4_K_M

Qwen3.6 35B-A3B at Q8_0 is the engine's second pick for this config and raises the precision floor. These 35B MoE models load at ~39GB in Q8, giving you near-lossless quality on the active parameters that do the reasoning. At ~160 tok/s est., they are fast enough for agent workflows and multi-step tool use. This is the daily driver for users who want the best possible quality at interactive speed.

ollama run qwen3.6:35b-a3b-q8_0

Nemotron 3 Super 120B-A12B (IQ4_XS) is NVIDIA's March 2026 open MoE: 120B stored with 12B active, built from Mamba-2 and MoE layers with only a few attention layers, and a context window of up to 1M tokens (model card). The Ollama Q4_K_M pull is about 87GB and does not fit, but unsloth's UD-IQ4_XS GGUF is about 64.5GB, so it loads with roughly 12GB to spare and runs ~79 tok/s est. The engine rates the fit heavy, so budget context carefully. IQ4_XS is slightly lower precision than Q4_K_M, and Ollama has no tag for this build, so run it through llama.cpp:

llama-server -hf unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:UD-IQ4_XS

Qwen3.5 122B-A10B is the largest MoE that cleanly fits in 96GB. Its 122B total parameters and 10B active set produce frontier-level reasoning scores, and at ~102 tok/s est. it remains usable for considered chat and batch analysis. The active parameter count is higher than the 80B-A3B, so raw throughput is lower, but the quality ceiling is higher for complex multi-step tasks.

ollama run qwen3.5:122b-a10b

GPT-OSS 120B, an August 2025 release that newer picks now outrank, loads at MXFP4 quantization in about 65GB, a heavy fit but usable for single-model sessions. Its 5.1B active parameter count delivers reasoning quality that competes with frontier proprietary models, at ~146 tok/s est. This is the model to load when you need a deliberate, high-quality answer and can trade the speed of the smaller MoE options.

ollama run gpt-oss:120b

Qwen3-Next 80B-A3B is the speed pick for this config. At ~50GB in Q4, it fits with 27GB of headroom for KV-cache and context. The 3B active parameter design means it generates at ~201 tok/s est., fast enough for interactive chat and real-time coding assistance. Its 128K native context window makes it a strong option for repository-level code analysis or long document work.

ollama run qwen3-next:80b

Llama 4 Scout (109B, MoE, 17B active) drops out of the ranked list: it loads at ~67GB in Q4_K_M, the tightest fit here, and at ~73 tok/s est. it is the slowest per token of the group, but its long-context handling and multimodal capabilities still make it the right choice for document-heavy, image-including workflows that need a single-model solution.

What 96GB Can't Run

A 96GB M5 Ultra cannot run 400B-class models like Llama 4 Maverick at all - that needs the 512GB tier. It also cannot run DeepSeek-R1 671B or Llama 3.1 405B at any usable quantization. Even Llama 3.3 70B at Q8 (75GB) is a very tight fit that leaves almost no room for context.

If your work regularly requires those model classes, step up to the 256GB or 512GB M5 Ultra configs. The 256GB tier gives you comfortable room for 120B-class MoE with long context and multi-model setups, while the 512GB tier opens up 400B Maverick and DeepSeek at Q4. Check the best LLM for 256GB M5 Ultra and best LLM for 512GB M5 Ultra for those configs. For most users, the 96GB config covers 70B Q4 dense and 80-120B MoE - the practical range for daily local AI work.

FAQ

Can the 96GB M5 Ultra run Llama 4 Maverick?

No. Llama 4 Maverick 400B needs about 245GB at Q4_K_M, which exceeds the 96GB capacity. The smallest config that runs Maverick is the 512GB M5 Ultra, where it loads at ~76 tok/s est.

Is 96GB enough for local LLMs in 2026?

Yes, for most practical work. The 96GB M5 Ultra runs 70B Q4 dense models with generous context headroom, and it loads 120B-class MoE models like GPT-OSS 120B and Qwen3.5 122B-A10B. The models it cannot run (400B-plus) are edge cases for most developers and researchers. If your ceiling is the 70-120B class, 96GB is the right amount.

M5 Ultra or M5 Max for local AI?

The M5 Max tops out at 128GB unified memory and ~614 GB/s bandwidth. The M5 Ultra doubles bandwidth to 1.2 TB/s and goes up to 512GB (the 512GB model ships from late October 2026; 96GB and 256GB shipped September 22). For 70B and smaller models, the M5 Max is capable and cheaper. For 80B-plus MoE models at usable speed, the M5 Ultra is the correct choice. The 96GB M5 Ultra ($5,499) is the entry point for Ultra-class performance.

Should I get the 96GB or 256GB M5 Ultra?

Choose 96GB if your work centers on 35-80B MoE models and 70B dense models with long context. Choose 256GB if you regularly run 120B-class MoE at long context, keep multiple large models loaded, or run agent workflows that need 64K-plus context. The 256GB tier costs more but gives roughly 3x the usable memory.

How fast is the 96GB M5 Ultra for a 7B model?

The ModelFit engine estimates ~136 tok/s on a 7B Q4 reference workload, roughly 45% faster than the M3 Ultra's ~94 tok/s. That speed class covers instant chat and real-time code completion with models like Qwen3 8B.