M5 Max vs M4 Max for Local LLMs: What the Throughput Gain Actually Buys

If you run models locally on a Mac, the M5 Max versus M4 Max decision is not really about the GPU core count or the Neural Engine, it is about memory bandwidth, because that is the number single stream decoding is bound by. Here is the comparison that matters, and then the reason it matters.

Property	M4 Max	M5 Max
Memory bandwidth	546 GB/s	600 GB/s
Bandwidth lift	baseline	about 10%
Decode bound by	memory bandwidth	memory bandwidth
Reported throughput gain (workload dependent)	baseline	up to high twenties percent with architecture gains
What decides the real number	model size, quantization, context	model size, quantization, context

The single most useful fact about local LLM performance on Apple Silicon is that generating tokens one at a time is memory bandwidth bound. To produce each token the hardware has to read the model's weights out of unified memory, and for a large model that read is the bottleneck, not the arithmetic. That is why decode throughput tracks memory bandwidth so closely, and why the M5 Max's move to 600 GB/s from 546 is the headline for inference even though the marketing leads with compute.

Why decode throughput follows memory bandwidth

The mechanics are worth stating plainly because they explain every number downstream. During generation the model reads its weights from memory for each token it produces. On a bandwidth bound workload, the theoretical ceiling on tokens per second is roughly the available memory bandwidth divided by the bytes of weights that must be read per token, which is set by the model's active parameter count and its quantization. Compute sits idle waiting for those reads, so adding compute does little; adding bandwidth moves the ceiling directly.

That relationship is the reason a ten percent bandwidth increase shows up as a roughly proportional decode speedup on models that are firmly bandwidth bound, and it is the same principle behind the broader Apple Silicon memory bandwidth and tokens per second analysis. It also explains why quantization matters so much: a 4 bit model reads a quarter of the bytes per token that a 16 bit model does, so it runs several times faster on the same bandwidth. The chip sets the ceiling; the quantization format you serve decides how close to it you get.

The generational gain is real but workload dependent

The clean version of the story is that 600 over 546 is about a ten percent bandwidth lift, so expect roughly ten percent more decode throughput on a firmly bandwidth bound model. The messier and more honest version is that the M5 Max also brings GPU and Neural Engine architecture improvements, including a faster path for the matrix operations that verification and prefill lean on, so some workloads report gains into the high twenties percent while others land near the bandwidth ratio. The spread is not noise, it is the different mix of prefill and decode, compute bound and bandwidth bound work, in different tasks.

A concrete cross generation data point makes the range visible. On Meta's Muse Glimmer with its DFlash drafter, the M5 Max produced 26.6 tokens per second at baseline against the M4 Max's 23.7, and with the drafter enabled 50.2 against 37.8. The baseline gap there is closer to twelve percent, in line with the bandwidth ratio plus some architectural help, while the gap with speculative decoding widens because the M5's faster verification path compounds the advantage. The takeaway is not a single multiplier, it is that the M5 Max's lead over the M4 Max grows on workloads that stress the parts Apple improved beyond raw bandwidth, and stays near the bandwidth ratio on plain decode.

What the extra throughput actually buys you

Translate the percentages into something you can decide on. On a plain decode workload, a ten to fifteen percent throughput gain turns a model that produced, say, 30 tokens per second into one that produces 33 to 35. That is a real quality of life improvement in an interactive session and a real cost per token improvement in a batch pipeline, but it is not a category change; a model that was too slow to be usable on the M4 Max is probably still too slow on the M5 Max. Where the gain compounds is when it stacks with software: enabling speculative decoding on top of the faster hardware, as the Glimmer numbers show, widens the generational gap because the M5 accelerates the verification step that speculative decoding hammers.

Memory capacity, not just bandwidth, is the other half of the buying decision and it is often the more important one. The largest model you can run at all is set by how much unified memory you have, and no amount of bandwidth helps a model that does not fit. If your constraint is running a bigger model rather than running your current model faster, the memory configuration is the decision and the generational bandwidth bump is secondary. This is the same tradeoff that shapes any local inference build across M chip tiers: capacity decides what you can run, bandwidth decides how fast it runs.

When the upgrade justifies itself, and when it does not

The honest guidance is that the M5 Max over M4 Max jump is an incremental win for inference, not a transformative one, and whether it is worth it depends on which constraint is binding. It justifies itself if you are buying a new machine anyway, if you run bandwidth bound decode all day and a ten to twenty percent speedup pays back in throughput, or if you lean on speculative decoding and want the faster verification path that widens the gap. It does not justify itself as an upgrade from a working M4 Max if your bottleneck is memory capacity rather than speed, because you should be buying more unified memory, not a newer chip at the same capacity. And it does not justify itself if your current setup is already fast enough for your workload, because a fifteen percent decode gain is not a reason to replace a machine that does the job.

When this applies to your stack

Size the decision by the constraint that actually binds you. If you run models locally and decode speed is your limit, the M5 Max's 600 GB/s buys a real, roughly proportional throughput gain over the M4 Max's 546, more on workloads that also stress the M5's improved compute path, and it compounds with speculative decoding. If your limit is the size of the model you can load, spend on unified memory capacity first and treat the generational bandwidth bump as a bonus. Benchmark your exact model, quantization, and context on both chips if you can, because the gain ranges from the bandwidth ratio to the high twenties percent depending on the mix of prefill and decode in your work.

Specifying local inference hardware to a real workload, deciding between a faster chip and more memory, and getting the software stack, quantization, and speculative decoding tuned so the hardware you bought actually delivers its throughput is the AI infrastructure work Contra Collective takes on when a team standardizes on Apple Silicon for local models. The spec sheet gives you a bandwidth number. Which number is your bottleneck is the question that decides the purchase.

FAQ

How much faster is the M5 Max than the M4 Max for local LLMs? On plain, bandwidth bound decode, expect roughly the bandwidth ratio, about ten percent, since the M5 Max offers 600 GB/s against the M4 Max's 546. Workloads that also stress the M5's improved GPU and Neural Engine path can report gains into the high twenties percent, so the real figure depends on the mix of prefill and decode in your task.

Why does memory bandwidth matter more than compute for inference? Because generating tokens one at a time is memory bandwidth bound: the hardware must read the model's weights from unified memory for each token, and for a large model that read is the bottleneck while compute waits. Adding bandwidth raises the ceiling on tokens per second; adding compute mostly does not.

Does quantization change the comparison? Yes, strongly. A 4 bit model reads a quarter of the bytes per token that a 16 bit model does, so it runs several times faster on the same bandwidth. The chip sets the ceiling and the quantization decides how close you get, so compare both chips at the quantization you will actually run.

Should I upgrade from an M4 Max to an M5 Max? Upgrade if decode speed is your bottleneck and a ten to twenty percent gain pays back, if you are buying new anyway, or if you rely on speculative decoding and want the faster verification path. Do not upgrade if your real limit is memory capacity, because you should buy more unified memory instead, and do not upgrade a machine that is already fast enough for your workload.

What decides the largest model I can run? Unified memory capacity, not bandwidth. Bandwidth sets how fast a model that fits will run; capacity sets whether it fits at all. If your goal is running a bigger model, the memory configuration is the decision and the generational bandwidth bump is secondary.

Bandwidth figures reflect published Apple Silicon specifications and the generational throughput gains reflect vendor and third party reporting as of August 2026, measured on specific models, quantizations, and context lengths; real gains vary widely by workload, so benchmark your own model and configuration before making a purchase decision.