M5 Max for Local AI: Complete Apple Silicon Benchmark Guide (2026)

ⓘ About these figures: speed and score numbers in this guide are LLMCheck index estimates (est.) derived from our published methodology — not lab measurements. Community-verified runs are invited via /contribute.

Why Memory Bandwidth Is Everything

According to the LLMCheck index, LLM inference is memory-bandwidth bound, not compute-bound. Every token generated requires reading the entire model's weights from memory once. The speed at which your Mac can shuttle data from Unified Memory to the GPU determines your tokens-per-second ceiling.

This is why the M5 Max’s 614 GB/s — 12% more than the M4 Max’s 546 GB/s — translates directly into decode speed: about 10–12% faster for the same model, a little less for small ones. The M5 generation’s larger gain is in prompt processing, covered below.

M5 Family Specs at a Glance

Estimated Performance: M5 Max vs M4 Max

Corrected 23 September 2026: an earlier version of this section presented a table of M5 Max vs M4 Max results as tests run by LLMCheck, showing gains of about 28%. LLMCheck does not run lab tests, and a 28% decode gain is not consistent with the two chips’ roughly 12% difference in memory bandwidth. The section now gives the index’s estimates, labelled as such.

Token generation on Apple Silicon is bound by memory bandwidth. The M5 Max has about 12% more of it than the M4 Max (614 vs 546 GB/s), so for the same model at the same quantization the LLMCheck index estimates decode roughly 7–12% faster — for example Qwen3.8-27B at ~29 vs ~26 tok/s (estimated, 4-bit). Small models gain slightly less, because a fixed per-token overhead dominates their decode time.

The M5’s larger improvement is in prompt processing, where the new GPU Neural Accelerators do the work: time to first token on long prompts falls far more than decode speed rises. The first independent reviews of the M5 Ultra show the same split — see M5 Ultra and M6, measured. Every figure for both chips is on the benchmarks page, marked with its provenance.

MLX Framework: The M5 Advantage

Apple’s MLX framework is purpose-built for Apple Silicon and it shows: it often decodes about 20% faster than llama.cpp for the same model — one published comparison measured 110 vs 90 tok/s for Qwen 3 30B-A3B on an M4 Max (arXiv 2601.19139).

• Why MLX is faster: It uses Metal kernels tuned for Apple’s GPU — including, on M5, the Neural Accelerators in each GPU core — and avoids the abstraction layers that llama.cpp uses for cross-platform compatibility.
• When to use MLX: If you are on Apple Silicon and prioritize raw speed. The MLX ecosystem includes mlx-lm for text generation, mlx-vlm for vision models, and mlx-whisper for audio.
• When to use llama.cpp/Ollama: If you need the broadest model compatibility, the Ollama model library, or plan to run on non-Apple hardware as well.

# Quick MLX benchmark on your M5 Mac
pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen3.5-30B-A3B-4bit \
  --prompt "Write a function to sort a linked list" \
  --max-tokens 500 --verbose

Neural Accelerators, Not the Neural Engine

The M5 generation’s AI upgrade is in the GPU: every GPU core now has a Neural Accelerator, dedicated matrix hardware. Apple quotes up to 4x faster LLM prompt processing for the M5 Pro and M5 Max than for the M4 Pro and M4 Max.

• Prompt processing (prefill): the big gain — long prompts and documents start answering far sooner. MLX reaches the Neural Accelerators through Metal.
• Token generation (decode): still bound by memory bandwidth, so it tracks the 12% bandwidth gain.
• The Neural Engine (16-core) is a separate block used by Core ML and Apple Intelligence; neither MLX nor llama.cpp runs LLM decode on it.

Model Recommendations per M5 Variant

Based on the LLMCheck index, here is what to run on each M5 chip:

M5 (16–32GB) — Daily Driver Models

• Gemma 4 12B — text, image and audio in about 7GB, estimated~17 tok/s
• GPT-oss 20B — OpenAI’s open MoE, fits 16GB, estimated~41 tok/s
• Qwen 3.5 9B — estimated~23 tok/s
• Qwen 3.6-35B-A3B at 32GB — estimated~59 tok/s

M5 Pro (24–64GB) — Power User Models

• Qwen 3.6-35B-A3B — the index’s #2 model, estimated~92 tok/s
• Gemma 4 26B-A4B — multimodal MoE, estimated~72 tok/s
• Qwen3.8-27B — the strongest dense model in the index, estimated~15 tok/s
• Qwen3-Coder-Next at 64GB — 80B coding MoE, estimated~49 tok/s

M5 Max (36–128GB) — Frontier Local AI

• KAT-Coder-V2.5 — the index’s #1, estimated~117 tok/s
• Qwen3.8-Flash-Next at 128GB — estimated~49 tok/s
• DeepSeek V4 Flash at 2-bit, 128GB — community~39 tok/s
• Llama 3.3 70B — estimated~12 tok/s

M5 Ultra (96–512GB, Mac Studio) — Research Grade

• MiMo-V2.6-Flash — estimated~42 tok/s
• GLM-5.3-Flash at 4-bit — estimated~35 tok/s
• GLM-5.3 at 512GB — estimated~17 tok/s
• Serve models to a local team through an OpenAI-compatible API

Not sure which M5 variant matches your needs? Use the Best-by-Mac rankings or the Mac Advisor for your exact configuration.

Single-stream decode at 4-bit (DeepSeek V4 Flash at 2-bit) from the LLMCheck index — estimated unless marked; Llama 3.3 70B and Qwen 3.5 9B columns below likewise.

Chip	GPU cores	Bandwidth	Max RAM	Llama 3.3 70B	Qwen 3.5 9B
M5 Ultra	64–80	1,228 GB/s	512 GB	estimated~23 tok/s	estimated~146 tok/s
M5 Max	32–40	614 GB/s	128 GB	estimated~12 tok/s	estimated~82 tok/s
M4 Max	32–40	546 GB/s	128 GB	estimated~11 tok/s	estimated~74 tok/s
M2 Ultra	60–76	800 GB/s	192 GB	estimated~15 tok/s	estimated~103 tok/s
M3 Max	30–40	400 GB/s	128 GB	estimated~8 tok/s	estimated~56 tok/s
M5 Pro	16–20	307 GB/s	64 GB	estimated~6 tok/s	estimated~44 tok/s
M4 Pro	16–20	273 GB/s	64 GB	estimated~5 tok/s	estimated~39 tok/s
M3 Pro	14–18	150 GB/s	36 GB	Doesn’t fit	estimated~22 tok/s