Apple · Apple M5 Max · 2026
Apple Mac Studio M5 Max 128GB for local AI
A quiet 128GB machine with more than twice the bandwidth of Strix Halo or DGX Spark.
Updated · Speeds marked ~ are estimates; seemethodology
- Unified memory
- 128GB
- Bandwidth
- 614 GB/s
- Power
- 480W
- Runs well
- 12 models
M5 Max 128GB
Typically about $5,100
Can the M5 Max 128GB run LLMs?
The M5 Max 128GB has 128GB of unified memory (about 96GB usable by the GPU by default) and 614 GB/s of memory bandwidth, which decides how big a model fits and how fast it generates text. It comfortably runs models up to about 122B parameters: the largest in our catalog is Qwen3.5 122B-A10B at Q4_K_M (77GB with 8K context, ~34 tokens/sec).
Recommendations
Best models for the M5 Max 128GB
Best overall · also coding, reasoning, vision, largest that fits
Qwen3.5 122B-A10BQ4_K_M · 77GB · ~34 tokens/sec
Runs wellRunner-up
Qwen3.6 35B-A3BQ8_0 · 41GB · ~56 tokens/sec
Runs wellFastest good model
gpt-oss-20bMXFP4 · 15GB · ~73 tokens/sec
Runs well
Compatibility
What runs on the M5 Max 128GB
Every current model in our catalog, at the best quantization that fits in the ~96GB the GPU can use with 8K tokens of context. "Partial offload" means part of the model runs from system RAM (we assume 64GB), which is much slower.
| Model | Parameters | Best quant | Memory | Speed | Verdict |
|---|---|---|---|---|---|
| Gemma 4 E4B | 8B | Q8_0 | 10 GB | ~40 tok/s | Runs well |
| Qwen3.5 9B | 9B | Q8_0 | 12 GB | ~35 tok/s | Runs well |
| Phi-4 | 14.7B | Q8_0 | 18 GB | ~25 tok/s | Runs well |
| gpt-oss-20b | 20.9B (3.6B active) | MXFP4 | 15 GB | ~73 tok/s | Runs well |
| Gemma 4 26B A4B | 26B (3.8B active) | Q8_0 | 30 GB | ~49 tok/s | Runs well |
| Qwen3.8 27B | 27B | Q8_0 | 32 GB | ~13 tok/s | Runs well |
| Gemma 4 31B | 31B | Q8_0 | 36 GB | ~12 tok/s | Runs well |
| Qwen3.6 35B-A3B | 35B (3B active) | Q8_0 | 41 GB | ~56 tok/s | Runs well |
| GLM-4.5-Air | 106B (12B active) | Q5_K_M | 80 GB | ~25 tok/s | Runs well |
| Llama 4 Scout | 109B (17B active) | Q5_K_M | 81 GB | ~19 tok/s | Runs well |
| gpt-oss-120b | 117B (5.1B active) | MXFP4 | 68 GB | ~64 tok/s | Runs well |
| Qwen3.5 122B-A10B | 122B (10B active) | Q4_K_M | 77 GB | ~34 tok/s | Runs well |
| DeepSeek V4 Flash | 284B (13B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3-Flash | 320B (18B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3 | 744B (40B active) | — | — | — | Won't fitWhat it needs |
Performance
M5 Max 128GB vs. alternatives on Qwen3.8 27B
Generation speed in tokens per second for a single user. Token generation is limited mostly by memory bandwidth, which is why bandwidth predicts these results so well.
M5 Max 128GB
Q8_0 · 128GB
~13tok/s
M4 Max 128GB
Q8_0 · 128GB
~12tok/s
DGX Spark
Q8_0 · 128GB
~6.9tok/s
Ryzen AI Max+ 395
Q8_0 · 128GB
~6.4tok/s
Measured
M5 Max 128GB benchmark results
| Model | Quant | Runtime | Generation | Prompt | Source |
|---|---|---|---|---|---|
| Llama 2 7B Chat | Q4_0 | llama.cpp (Metal) | 120 tok/s | 3220 tok/s | llama.cpp discussion #4167: Performance of llama.cpp on Apple Silicon M-series · October 2026 |
| Llama 2 7B Chat | Q8_0 | llama.cpp (Metal) | 72 tok/s | 3145 tok/s | llama.cpp discussion #4167: Performance of llama.cpp on Apple Silicon M-series · October 2026 |
| Llama 2 7B Chat | F16 | llama.cpp (Metal) | 37 tok/s | 3160 tok/s | llama.cpp discussion #4167: Performance of llama.cpp on Apple Silicon M-series · October 2026 |
Our take
Should you buy the M5 Max 128GB for AI?
The M5 Max Mac Studio with the 40-core GPU and 128GB of unified memory delivers 614 GB/s of bandwidth, up from 546 GB/s on the M4 Max. About 96GB is available to the GPU by default, enough for 70B models at 6 bit or MoE models like gpt-oss-120b with room for context. That bandwidth is more than double what Strix Halo and DGX Spark offer, so it generates tokens noticeably faster on the same models. At about $5,100 in October 2026 it costs more than a Strix Halo mini PC but less than a DGX Spark.
What we like
- 614 GB/s, more than twice Strix Halo and DGX Spark
- 128GB of unified memory in a quiet desktop
- Strong MLX support
Watch out for
- No CUDA support
- Prompt processing slower than NVIDIA GPUs
- Memory is fixed at purchase
Best for: Running 70B and 120B MoE models quietly · Mac developers who want local models on their main machine
Specifications
M5 Max 128GB specs that matter for AI
- Memory
- 128GB LPDDR5X (unified)
- GPU-usable memory
- ~96GB by default
- Memory bandwidth
- 614 GB/s
- Architecture
- Apple M5 Max
- Power
- 480W (system)
- Interface
- Thunderbolt 5 (no PCIe slots)
- Released
- 2026
- Street price (new)
- ~$5,100 (2026-10)
- Software
- Metal, MLX
macOS lets the GPU use about 75 percent of memory by default (about 96GB), and the iogpu.wired_limit_mb setting can raise it. The 614 GB/s figure applies to the 40-core GPU version of M5 Max.
Sources: Apple Mac Studio (M5 Max or M5 Ultra) tech specs, PromptQuorum: M5 Pro vs M5 Max bandwidth and LLM speed, ModelPiper: raising the Metal memory ceiling with iogpu.wired_limit_mb
Cross-shop
Alternatives to the M5 Max 128GB
Frequently asked questions
How much VRAM does the M5 Max 128GB have?
The Apple Mac Studio M5 Max 128GB has 128GB of unified memory (about 96GB usable by the GPU by default) with 614 GB/s of memory bandwidth.
What is the largest LLM the M5 Max 128GB can run?
Fully in GPU memory, the largest model in our catalog that runs comfortably is Qwen3.5 122B-A10B (122B (10B active)) at Q4_K_M, using about 77GB with 8K context at ~34 tokens/sec.
Can the M5 Max 128GB run Llama 3.3 70B?
Yes. At Q8_0 it needs about 81GB with 8K context and runs at ~5.3 tokens/sec.
What is the best LLM to run on the M5 Max 128GB?
Our top pick is Qwen3.5 122B-A10B at Q4_K_M, which fits in 128GB with room for 8K of context and runs at ~34 tokens/sec. See our full list of the best models for the M5 Max 128GB for coding, reasoning, and vision picks.
Is the M5 Max 128GB good for local AI in 2026?
A quiet 128GB machine with more than twice the bandwidth of Strix Halo or DGX Spark.