Qwen3 32B on Apple M4 Max vs M4 Pro: tok/s and Memory per Quant (2026)

Introduction

If you are shopping for a MacBook Pro to run local language models, the M4 Pro and M4 Max split the lineup in two. The M4 Pro tier starts at 24GB of unified memory and tops out at 64GB, per Apple. The M4 Max tier comes in a binned version (14-core CPU, 32-core GPU) and a full version (16-core CPU, 40-core GPU) with up to 128GB. Apple's current MacBook Pro lineup uses newer chips, so M4 machines are previous-generation models; two of the listings tracked here are the M4 Max MacBook Pro (14-core CPU, 32-core GPU, 36GB) and the renewed 16-inch M4 Pro with 24GB tracked here.

Qwen3 32B is a good test of the split. It is a dense 32-billion-parameter model, large enough to need real memory, popular for coding and reasoning, and small enough that both chips can run it with the right configuration.

Step 0: does your configuration fit 32B at Q4 at all?

Check memory before speed. Qwen3 32B at Q4_K_M needs about 20GB for the weights alone (arithmetic below). macOS also needs memory for itself and limits how much unified memory the GPU can wire by default. You can raise that limit with sudo sysctl iogpu.wired_limit_mb=<MB>, but on a 24GB machine there isn't much left to give.

• 24GB (M4 Pro base): Q3_K_M fits with modest context. Q4_K_M only fits with a raised GPU memory limit, a short context, and few other apps open.
• 36GB (binned M4 Max): Q4_K_M and Q5_K_M fit with real context headroom.
• 48GB+ (M4 Pro 48GB, M4 Max 48/64/128GB): Q6_K and Q8_0 fit, with long context.

If your budget only reaches a 24GB M4 Pro, plan around Qwen3 14B as your daily model and 32B as an occasional one.

Key Takeaways

• Bandwidth decides generation speed. M4 Pro: 273GB/s. M4 Max: up to 546GB/s on the 40-core GPU and 410GB/s on the binned 32-core GPU, per Apple and the llama.cpp #4167 table.
• Measured ratio on the same benchmark: Llama 2 7B Q4_0 generates at 50.74 tok/s on a 20-core M4 Pro, 69.95 tok/s on the 32-core M4 Max and 83.06 tok/s on the 40-core M4 Max (llama.cpp #4167).
• Qwen3 32B Q4_K_M: LLMCheck lists 23 tok/s on the M4 Max (MLX) and 12 tok/s on a 32GB M4 Pro (Ollama). LLMCheck flags both rows as estimates, not measured runs.
• Prefill is the bigger gap. The 40-core M4 Max processes prompts at about 2x the 20-core M4 Pro's rate in #4167 (885.68 vs 439.78 tok/s on 7B Q4_0).
• 24GB is the problem, not the M4 Pro chip. An M4 Pro with 48GB runs 32B comfortably, just more slowly.

Spec-delta table

Sources: bandwidth and maximum memory come from Apple's M4 Pro and M4 Max announcement, which lists the M4 Max's 546GB/s as an "up to" figure. The binned M4 Max's 410GB/s and the GPU-core splits come from the chip table in llama.cpp discussion #4167. For current pricing, use the live price buttons. M4 MacBook Pro prices now vary widely between new-old-stock and renewed listings.

Per-quant table for Qwen3 32B

Memory figures are arithmetic: Qwen3 32B's roughly 32.8B parameters times each GGUF quant's average bits per weight (about 3.9 for Q3_K_M, 4.85 for Q4_K_M, 5.7 for Q5_K_M, 6.56 for Q6_K, 8.5 for Q8_0), divided by 8. Add about 1GB of runtime overhead and the KV cache for your context.

The closest measured 32B figure on an M4 Pro comes from a MacRumors forum owner running Qwen2.5 32B on a 64GB M4 Pro Mac mini: "around 11 tokens/second". That sits close to LLMCheck's 12 tok/s estimate for the M4 Pro. No equivalent measured Qwen3 32B run on an M4 Max could be found as of November 2026. The 23 tok/s estimate is in line with what the 7B bandwidth ratio would predict.

Same-hardware context rows

These are public runs collected on the Apple M4 Max and Apple M4 Pro benchmark pages. Each row names its source. They are not SpecPicks measurements.

Two patterns stand out. First, generation speed tracks bandwidth closely: the 40-core M4 Max has 2.0x the 20-core M4 Pro's bandwidth and generates 1.64x faster on 7B Q4_0, and 1.76x faster on Q8_0, where the larger weights make the run more memory-bound. Second, prefill tracks GPU cores: 40 cores vs 20 cores gives about 2.0x prefill speed.

Applying the measured 7B Q4_0 ratios to the M4 Pro's roughly 11-12 tok/s on 32B gives a rough estimate of about 15-17 tok/s for the binned M4 Max and about 18-20 tok/s for the full M4 Max under llama.cpp. MLX typically runs faster still. These are estimates, labelled as such.

MLX vs llama.cpp vs Ollama on Apple silicon

The same chip shows very different tok/s depending on the runtime, which is why every row above names one.

• MLX is Apple's own array framework, written for unified memory and Metal. For generation on Apple silicon it is usually the fastest option.
• llama.cpp runs GGUF files on its Metal backend. It offers the widest choice of quants (Q3_K_M through Q8_0, plus IQ quants) and the most reproducible benchmark tool, llama-bench.
• Ollama wraps llama.cpp (and, in recent versions, an MLX backend) with model management and an API. It is convenient, but defaults and version differences move the numbers.

How big the gap gets: Markaicode's Qwen 3.5 throughput test on an M4 Max measured Qwen3.5 35B-A3B (a mixture-of-experts model) at 43.5 tok/s in Ollama and 130.2 tok/s in MLX on the same 128GB machine. On an M4 Pro, Contra Collective reports Qwen2.5 14B 4-bit at 52-58 tok/s under MLX, while heyuan110.com reports 24 tok/s for Qwen2.5 14B Q4_K_M under Ollama on a 48GB M4 Pro Mac mini. The runtime can matter as much as the chip. Compare chips only within one runtime.

Prefill vs generation on unified memory

Apple silicon's strength is capacity: 36GB to 128GB of memory the GPU can use directly, which no single consumer graphics card offers. Its weakness is prefill, the phase where the model reads your prompt before answering.

In #4167, the 40-core M4 Max processes a 512-token prompt on Llama 2 7B Q4_0 at 885.68 tok/s, and the 20-core M4 Pro at 439.78 tok/s. Prefill scales with model size, so on a 32B model expect roughly a quarter of those rates. Pasting a 10,000-token document into Qwen3 32B can take more than a minute on an M4 Pro before the first answer token appears, and about half that on the full M4 Max. For coding agents and RAG pipelines that send long contexts repeatedly, this is the M4 Max's strongest argument, more than the generation speed gain.

Context-length impact: what 24GB leaves at 32B Q4

Qwen3 32B uses 64 layers with 8 key-value heads of dimension 128. At FP16, its KV cache costs about 2 × 64 × 8 × 128 × 2 bytes, or 0.25MB per token: about 2GB at 8K tokens and 8GB at 32K.

On a 24GB M4 Pro running Q4_K_M:

• About 20GB for weights plus about 1GB of runtime overhead leaves about 3GB of unified memory, before macOS takes its share.
• In practice that means a context of a few thousand tokens, and other apps get squeezed.
• 8-bit KV cache (supported in llama.cpp and MLX) roughly doubles the context you can fit.

On a 36GB binned M4 Max, the same model leaves around 10-12GB after a reasonable system reserve: 32K+ tokens with 8-bit KV cache. Context capacity, not speed, is the main reason to avoid the 24GB tier for 32B work.

Perf-per-dollar, plus a PC baseline

At tracked listing prices at the time of writing, the 36GB binned M4 Max MacBook Pro costs roughly 1.5-1.9x as much as the 24GB M4 Pro configurations (new and renewed), and delivers roughly 1.4x the generation speed on the measured 7B Q4_0 benchmark. On tokens per dollar, the two are close. The M4 Max's real value is the 36GB, which makes 32B usable rather than possible. A renewed 14-inch M4 Pro with 24GB is the cheapest way into the M4 Pro tier, but it inherits the 24GB limits above.

For a PC comparison, the MSI RTX 3060 12GB has a median of about 29.4 tok/s across 27 published 12-15B model runs from 12 sources on our RTX 3060 12GB benchmark page. That is faster than the M4 Pro's 26 tok/s on Qwen3 14B in LLMCheck's data, for a fraction of the price. The 12GB card can't hold Qwen3 32B at Q4 (about 20GB) without offloading layers to system RAM, and offloading cuts generation speed sharply. For 14B models, the PC wins on price. For 32B on one device, the Mac wins on capacity.

Verdict matrix

Get the M4 Pro if…

• Your daily models are 7B-14B and 32B is occasional
• You can buy the 48GB configuration, which removes the 24GB squeeze
• Portability and battery life matter more than prompt speed

Get the M4 Max if…

• Qwen3 32B (or similar dense 27-32B models) is your daily model
• You work with long contexts: RAG, codebases, agent loops
• You want room to try 70B models (64GB+ configurations; Markaicode measured 12.53 tok/s on Llama 3.1 70B Q4_K_M)

Get a PC GPU instead if…

• Your models fit in 12-24GB of VRAM and you want the fastest prefill per dollar
• You depend on CUDA-only tools
• You don't need a laptop

Recommended pick

For Qwen3 32B specifically, the M4 Max with 36GB or more is the right machine. The binned 36GB model gives enough memory for Q4_K_M with real context and roughly 1.4x the M4 Pro's generation speed on measured runs. If you can't stretch to the M4 Max, buy the M4 Pro with 48GB, not 24GB. Bandwidth costs you speed; too little memory means the model doesn't run usefully at all.

Spec comparison and related guides

• Run Qwen 3 14B on an M4
• Run Qwen 3 32B on an M4 Pro
• Run Qwen 3 32B on an M4 Max

Live price comparison

Compare the two MacBook Pro tiers side by side, with live prices, on the M4 Max vs M4 Pro MacBook Pro comparison page.

Citations and sources

1. Apple Newsroom — Apple introduces M4 Pro and M4 Max (accessed 2026-11-09)
2. llama.cpp discussion #4167 — Performance of llama.cpp on Apple Silicon (accessed 2026-11-09)
3. Markaicode — llama.cpp on M4 Max benchmark (accessed 2026-11-09)
4. LLMCheck — Apple silicon LLM benchmarks (accessed 2026-11-09)
5. Markaicode — Qwen 3 throughput on M4 Max (accessed 2026-11-09)
6. Contra Collective — M4/M5 Pro local AI inference with MLX (accessed 2026-11-09)
7. heyuan110.com — Mac Apple silicon AI workstation (accessed 2026-11-09)
8. MacRumors Forums — M4 Pro local AI thread (accessed 2026-11-09)

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.