Best LLM for Mac Studio M5 Max with 48GB RAM (2026)

The 48GB Mac Studio M5 Max is the MoE sweet spot of the lineup. At 48GB with the full 614 GB/s memory bus, it unlocks the 35B MoE class with real context headroom. It starts at $2,499 for the base chip, and the 40-core GPU variant at $3,099 keeps the same memory. This is the machine for developers and researchers who want fast MoE inference without paying for dense 70B memory or the M5 Ultra's higher entry price.

TL;DR: The 48GB M5 Max runs every 35B MoE comfortably. Qwen3.6 35B-A3B (~163 tok/s est.) leads the engine's ranking, with Nemotron 3.5 Lightning 30B-A3B (~103 tok/s est.) and GLM-4.7-Flash 30B-A3B (~116 tok/s est.) behind it. Gemma 4 26B-A4B at Q8_0 (~28.1GB, ~75 tok/s est.) is the multimodal pick and Gemma 4 31B (~24 tok/s est.) the dense quality option. For coding agents, Ornith 1.0 35B (~166 tok/s est.), Flint 40B-A2.5B (~175 tok/s est., the fastest pick on the list) and Laguna XS 2.1 (~153 tok/s est.) round out the eight.

Estimated token generation on the Mac Studio M5 Max 48GB. ModelFit engine estimates.

The Mac Studio device page covers the full hardware comparison. This guide ranks the models that shine at 48GB and the ones that need more memory.

How 48GB Shapes Your Model Choices

With 48GB of unified memory, your usable budget climbs well above the 36GB tier. The full 614 GB/s bus also removes the bandwidth bottleneck of the binned entry config.

At Q4_K_M, a 35B dense model loads ~21GB, and a 35B MoE loads ~20-22GB. That leaves 10-15GB for KV-cache, which is enough for 32K-64K context on MoE models. The math is straightforward: 48GB is the first tier where 35B MoE models feel roomy rather than tight.

The 614 GB/s bus (vs 460 GB/s on the 36GB config) adds roughly 33% more bandwidth. In tok/s terms, every model on this tier runs faster than it would on the binned 36GB chip, MoE and dense alike, because decode speed tracks bandwidth: Qwen3.5 35B-A3B goes from ~131 to ~166 tok/s est.

This is also the first tier where you can run two smaller models side by side. A Qwen3.5 35B-A3B and a Gemma 4 26B sit in memory together for multi-agent or hybrid workflows.

Best Models Ranked

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

Model Details

1. Qwen3.6 35B-A3B is the engine's top pick for this config. It loads ~22GB, slightly heavier than the 3.5 generation, but the quality lift shows on agentic coding and structured output. Expect ~163 tok/s est., close to the 3.5 on decode speed.

ollama run qwen3.6:35b-a3b

2. Nemotron 3.5 Lightning 30B-A3B is NVIDIA's hybrid Mamba-MoE: 30B stored, 3B active, and only 6 of its 52 layers use full attention, so the KV-cache stays a fraction of a dense 30B's and long contexts stay cheap. It loads at ~25.4GB and runs ~103 tok/s est.

ollama run nemotron-3.5-lightning:30b

3. GLM-4.7-Flash 30B-A3B is Z.ai's local-class MoE (MIT, 3B active). It scores 59.2 on SWE-bench Verified per its model card, and at ~19GB it is the lightest 30B-class model here, at ~116 tok/s est.

ollama run glm-4.7-flash

4. Gemma 4 26B-A4B at Q8_0 is Google DeepMind's multimodal MoE, Apache 2.0, with 4B parameters active per token, text and image input and a context window of up to 256K tokens (model card). The Q8_0 build loads ~28.1GB, leaving about 7GB of the ~35GB budget for context, and runs ~75 tok/s est. Pick it when the work mixes text and images and you want Q8 precision.

ollama run gemma4:26b-a4b-it-q8_0

5. Gemma 4 31B is the dense member of the same family, also Apache 2.0 with text and image input and up to 256K tokens of context (model card). It loads ~20GB at Q4_K_M and runs ~24 tok/s est.: every parameter is read for each token, so it is slower than the MoE picks, in exchange for dense consistency on long writing and analysis.

ollama run gemma4:31b

6. Ornith 1.0 35B is a 35B MoE built for coding agents. At ~21.2GB and ~166 tok/s est., it sits close to the fastest pick on this list. It shines in tool-calling scenarios.

ollama run ornith:35b

7. Flint 40B-A2.5B is Recursive's coding-agent MoE: 39.8B parameters stored, about 2.5B active per token, released under Apache 2.0 (model card). Treat it as a coding and agent pick, not a general chat model. The card reports 74.2% on SWE-bench Verified and 54.9% on Terminal-Bench 2.1, both publisher-reported. The Ollama pull is an NVFP4 build of about 23GB with a 256K context window (Ollama library), which leaves about 12GB of the 35GB budget for context. At ~175 tok/s est. it is the fastest pick on this list.

ollama run flint:40b

8. Laguna XS 2.1 is a 33B MoE specialized for agentic coding. At ~20.3GB and ~153 tok/s est., it trails the Qwen 35B models on speed by a small margin. Its design emphasizes long-horizon tasks where the agent must maintain reasoning across many tool calls.

ollama run laguna-xs-2.1:q4_K_M

Two earlier picks now rank outside the eight. Qwen3.5 35B-A3B Instruct is the earlier generation of the top pick: ~20GB loaded and ~166 tok/s est., still a capable daily driver for chat, coding and reasoning (ollama run qwen3.5:35b-a3b). Qwen3.6 27B is the dense Qwen flagship at ~18GB and ~30 tok/s est., the choice for long-document analysis when you want a dense model from the Qwen line (ollama run qwen3.6:27b).

Qwen3 30B, an older MoE (3B active) at ~22GB, drops out of the ranked list: at ~124 tok/s est. it still runs ahead of GLM-4.7-Flash and Nemotron 3.5 Lightning and keeps strong coding and chat quality, but the engine now ranks the newer picks above it.

What 48GB Cant Run

With more capacity, the boundaries shift to Q8 quantization and dense 70B models:

• Gemma 4 26B-A4B (Q8) needs 28.1GB: it fits and ranks fourth, but leaves only about 7GB for context, so long-context work belongs on the Q4_K_M build (~16GB).
• Qwen3.6 27B (Q8) needs 30GB, heavy for this tier.
• Mixtral 8x7B Instruct (46.7B parameters, 30GB at Q4) fits but fills most of your budget with no room for context. Skip it.
• Qwen3.6 35B-A3B (Q8) needs 38.7GB. That is too tight for 48GB.
• Qwen3.5 35B-A3B Instruct (Q8) needs 38.7GB. Same limit.

For 70B dense models at Q4, you need at least the 64GB M5 Max. For 120B MoE, jump to the 128GB M5 Max or M5 Ultra.

FAQ

Can the 48GB M5 Max run a 70B model?

No. A 70B model needs roughly 42GB at Q4, which nearly fills your entire budget. The 48GB tier is correct for 35B MoE and 27B dense. For 70B, step up to the 128GB M5 Max or the M5 Ultra.

How does the 48GB M5 Max compare to the 36GB?

The jump is significant. You get the full 614 GB/s bus (vs 460 GB/s) and 35B MoE models fit with real context headroom. The 36GB config handles 27B dense well, but the 48GB opens the 35B MoE class properly.

Is 48GB enough for running two models at once?

Yes, barely. Two 35B MoE models at Q4 each take ~20-22GB, totaling 40-44GB. That leaves 4-8GB for macOS plus KV-cache. Running a 35B MoE alongside a 9B model is more comfortable and does not require aggressive context limits.

What is the best model for coding on the 48GB M5 Max?

Qwen3.6 35B-A3B for general coding, Flint 40B-A2.5B and Ornith 1.0 35B for coding-agent loops, and Laguna XS 2.1 for long-horizon tool-calling workflows. Each fits comfortably at this tier.

Does the 48GB tier always have the 614 GB/s bus?

Yes. The 460 GB/s bus is specific to the 36GB binned config. All 48GB and higher M5 Max configurations ship with the full 614 GB/s memory bandwidth.