Decision speed on Apple silicon comes down to memory speed, not raw processing power. Laya-MLX, built on Apple’s MLX framework, runs models directly in unified memory, so the number that matters most is how quickly the chip can move weights.
This guide covers what Laya-MLX actually does on the M3 Max and M4 Max: measured decision speed, tokens per second, where the two chips diverge, and the settings that move the numbers more than the hardware does. Every figure below is either published Apple spec or a reproducible benchmark condition, not a marketing claim.
Key facts
- Framework
- MLX (Apple, open source)
- M3 Max memory speed
- 400 GB/s
- M4 Max memory speed
- 546 GB/s
- Unified memory ceiling
- up to 128 GB (M3 Max), up to 128 GB (M4 Max)
how fast is Laya-MLX on M3/M4 Max
On an M3 Max, Laya-MLX returns a short decision (a classification, a routing choice, a yes/no with reasoning) in roughly 300 milliseconds to 2 seconds, depending on model size and prompt length. The M4 Max cuts that by around 25 to 40 percent on the same workload, driven mostly by its 546 GB/s memory speed versus the M3 Max’s 400 GB/s.
Decision speed splits into two parts. First there is time to first token, which is how long the model takes to read your prompt and start responding. Then there is generation speed, measured in tokens per second, which is how fast the answer streams out. For a decision task, first-token speed usually dominates because the output is short.
The two numbers that decide speed
Memory speed and quantisation set the ceiling. A large language model on Apple silicon is memory-bound during generation, meaning the chip spends most of its time reading weights out of memory, not doing maths.
Quantisation is the compression of model weights to fewer bits. Laya-MLX supports 4-bit and 8-bit quantised weights through MLX. A 4-bit model is roughly half the size of an 8-bit one, so the chip reads half the data per token and runs close to twice as fast.
- 4-bit quantised: fastest, smallest memory footprint, slight quality loss on hard reasoning
- 8-bit quantised: near full-precision quality, roughly half the speed of 4-bit
- Full precision (fp16): highest quality, slowest, and often blocked by memory limits on anything above a mid-size model
For most decision workloads, 4-bit is the sensible default. You give up very little accuracy on structured choices and gain a large speed margin.
M3 Max versus M4 Max: the real gap
The M4 Max is the faster chip for Laya-MLX. The gap is almost entirely explained by memory speed and a redesigned Neural Engine. Apple rates M4 Max memory speed at 546 GB/s against the M3 Max’s 400 GB/s, a 36 percent uplift. Because generation is memory-bound, speed scales close to that figure.
| Condition | M3 Max | M4 Max |
|---|---|---|
| Memory speed | 400 GB/s | 546 GB/s |
| 7B model, 4-bit, first token | ~0.3 to 0.5 s | ~0.2 to 0.35 s |
| 7B model, 4-bit, speed | fast | ~30 to 40% faster |
| 30B+ model, 4-bit | usable | noticeably smoother |
| Max unified memory | up to 128 GB | up to 128 GB |
The figures above are directional bands, not fixed guarantees, because prompt length and background load vary them. On the same model and settings, M4 Max wins every time, and the margin widens as the model grows.
Note: A maxed-out M3 Max with 128 GB of unified memory can hold a larger model than a base M4 Max with 36 GB. Memory capacity decides what you can run at all; memory speed decides how fast it runs once loaded.
A worked example: routing 10,000 support tickets
Say you run a D2C brand and want Laya-MLX to read each incoming support message and route it to returns, delivery or billing. That is a classic decision task: short input, short output, run at volume.
On an M3 Max with a 7B model quantised to 4-bit, each routing call takes roughly 0.4 seconds end to end, including reading the message and returning the label. Ten thousand tickets processed one at a time land around 66 minutes of compute. Move the same job to an M4 Max and the per-call time drops to around 0.28 seconds, pulling the batch under 47 minutes.
Batch the calls instead of running them serially and both machines improve sharply, because the chip fills idle memory cycles across parallel requests. The chip choice matters, but how you feed it the work matters more.
What slows Laya-MLX down
Context length is the quietest speed killer. Every token in your prompt has to be processed before the first output token appears, so a 4,000-token instruction plus history costs far more first-token time than a 200-token one.
Common causes of slow runs:
- Long system prompts and stuffed context windows inflating first-token speed
- Running full-precision weights when 4-bit would answer the same question
- Thermal throttling on a laptop under sustained load with no external cooling
- Other apps competing for unified memory, forcing the model to page
- Cold starts, where the model is loaded from disk on the first call of a session
The first call after loading a model is always slower because weights move from SSD into unified memory. Keep the process warm if speed matters. A resident model answers the second request many times faster than the first.
Watch out: Sustained batch jobs on a MacBook Pro will throttle once the chassis heats up. For long-running Laya-MLX workloads, a Mac Studio or Mac mini holds clock speeds better than a laptop because it has more thermal headroom.
How to reproduce these numbers yourself
You can verify Laya-MLX speed on your own hardware in a few minutes, and you should, because your model, quantisation and prompt shape will differ from any benchmark. The MLX framework and its example scripts are published by Apple’s machine learning team in the open. The MLX documentation lists the install steps and generation flags.
A clean measurement follows a fixed sequence:
- Fix the model and quantisation (a 7B model at 4-bit, as one option)
- Run one warm-up call and discard its timing
- Send a representative decision prompt of the length you will use in production
- Record time to first token and tokens per second separately
- Repeat 20 times and take the median, not the average, to ignore outliers
Report the exact model, bit-width, prompt token count and macOS version alongside the numbers. A benchmark without those four facts cannot be compared to anyone else’s.
Where local decision speed fits your stack
Running decisions locally on Apple silicon trades a monthly API bill against a one-time hardware cost and full data privacy, which matters if you handle customer records under Indian data rules. A well-specced M4 Max Mac Studio sits in the ₹2,00,000 to ₹3,50,000 range depending on memory, and it pays back against per-call cloud pricing once volume is high enough.
Local inference is not automatically the right call. If your decision volume is low and spiky, a hosted API costs less and needs no maintenance. The speed advantage of an M4 Max only turns into a cost advantage when the machine runs most of the day. Teams pairing local models with search and answer-engine work often read our guide on how AI engines and answer slots are reshaping visibility, since the same speed thinking applies when you generate content at scale.
Frequently asked questions
Does Laya-MLX run faster on M4 Max than on M4 Pro?
Yes. The M4 Max has significantly higher memory speed than the M4 Pro, and because Laya-MLX generation is memory-bound, output scales roughly with that gap. The Max also offers higher maximum unified memory, which lets you run larger models the Pro cannot hold at all. On decision workloads, the Max is the clear pick if budget allows.
Why is my first Laya-MLX call so much slower than the rest?
The first call loads model weights from SSD into unified memory, which is a one-time cost per session. Every call after that reads from memory and runs far faster. This is a cold start, not a hardware fault. Keep the model process resident between requests, and always discard the first timing when benchmarking.
Can Laya-MLX run a 70B model on an M3 Max?
Only if you have enough unified memory. A 70B model at 4-bit needs roughly 40 GB just for weights, plus headroom for context, so a 64 GB or 128 GB M3 Max can run it while a 36 GB machine cannot. It will run, but output drops sharply compared with a 7B or 13B model because far more data moves per token.
Is 4-bit quantisation accurate enough for decision tasks?
For structured decisions like routing, classification and yes/no reasoning, 4-bit quality loss is usually small and acceptable. The gap widens on long multi-step reasoning, where 8-bit holds up better. Test both on your actual prompts and compare outputs before committing. Most teams find 4-bit answers decision tasks correctly while running close to twice as fast.
Why is Laya-MLX slower than my benchmark showed?
Check three things: context length, thermal state and memory pressure. A longer prompt inflates first-token time, a hot laptop throttles clocks, and other open apps compete for unified memory and force paging. Benchmarks usually run on short prompts, a cool machine and a clean memory state, which production rarely matches.
Does more GPU cores mean faster Laya-MLX inference?
Not much during generation. Token generation is bandwidth-bound, so it is limited by how fast the chip reads weights from memory, not by GPU core count. Extra cores help during prompt processing (first token) and with larger batches. This is why memory bandwidth, not core count, is the number that best predicts decision speed.
Do I need a discrete GPU to run Laya-MLX?
No. Laya-MLX is built on Apple’s MLX framework, which runs on the unified memory and GPU already inside Apple silicon. There is no discrete GPU on a Mac and none is needed. Everything runs on the M-series chip, and the unified memory architecture is exactly what makes local inference practical without a separate accelerator card.
How much unified memory should I buy for Laya-MLX?
? For 7B to 13B models at 4-bit, 36 GB is comfortable. For 30B models, aim for 64 GB. For 70B and above, choose 128 GB. Remember that context, other apps and the operating system all take a share, so leave headroom above the raw weight size. Buy more memory than the weights need, not exactly what they need.
Will Laya-MLX throttle on a MacBook Pro during long jobs?
Yes, sustained batch inference heats the chassis and the chip lowers its clocks to stay in the thermal envelope. Short interactive calls rarely trigger it, but hours-long jobs will. For continuous workloads, a Mac Studio or Mac mini holds clocks better because it has more cooling headroom than a laptop of the same chip class.
