Quick Verdict
If you want the answer before the details: upgrade to M5 if prompt processing or long-context work is central to how you use local AI. Coding assistants chewing through a repository, RAG pipelines stuffing thousands of tokens of retrieved context into the prompt, agentic loops re-reading state — these all lean heavily on the prefill phase, and that is exactly where the M5's Neural Accelerators shine.
If you mostly do back-and-forth chat with short prompts, the picture is more muted. Token generation — the part where the model streams its reply one token at a time — is bound by memory bandwidth, and the M5 only adds roughly 10% bandwidth per tier. A 15–30% generation improvement is real and pleasant, but rarely worth retiring a perfectly good M4. In that scenario, RAM capacity and bandwidth tier matter far more than the chip generation: a 64 GB M4 Max will out-run a 24 GB M5 base for serious local-LLM work every time.
According to LLMCheck benchmarks, the M5's prefill speedup is the headline win for local AI — long prompts and codebases load noticeably faster — while generation throughput tracks the modest per-tier bandwidth bump of about 10%.
What Actually Changed in the M5
Two things changed that matter for local LLMs, and a lot of things changed that do not.
1. Neural Accelerators in every GPU core
This is the big architectural shift. Previous Apple Silicon ran LLM matrix math on general-purpose GPU shader units. The M5 generation embeds a dedicated Neural Accelerator into each GPU core, purpose-built for the matrix-multiply operations that dominate transformer compute. Because prompt processing (prefill) is compute-heavy — the GPU has to crunch through every token of your prompt in parallel before generation begins — this is where you feel the accelerators most. Long-context work that used to make you wait several seconds before the first token now starts much faster.
2. About 10% more memory bandwidth per tier
Token generation, by contrast, is memory-bandwidth bound: each new token requires streaming a large slice of model weights out of Unified Memory. The M5 lifts bandwidth roughly 10% per tier, which translates fairly directly into faster generation. It is a steady, predictable gain — not a revolution.
| Tier | M4 Bandwidth | M5 Bandwidth |
|---|---|---|
| Base M4 / M5 | ~120 GB/s | ~150 GB/s |
| Pro | ~273 GB/s | ~273+ GB/s |
| Max | ~546 GB/s | ~600 GB/s |
What did not change is the RAM ceiling. Both generations top out at up to 128 GB of Unified Memory on laptop configurations, so the M5 does not let you load any model the M4 could not. The largest model you can run is decided by RAM, full stop — the M5 simply runs the same models faster, especially during prefill.
Benchmark Deltas: M4 vs M5
Here is how the generations compare across tiers, using a representative mid-size model at Q4 quantization. The generation column is the streaming tok/s you feel during a reply; the prefill column reflects how quickly a long prompt is consumed before the first token appears.
| Tier | Generation (tok/s) | Prefill speedup |
|---|---|---|
| Base M4 → M5 | +15–25% | Larger |
| Pro M4 → M5 | +15–20% | Larger |
| Max M4 → M5 | +20–30% | Largest |
Two patterns stand out. First, the generation gains roughly track each tier's bandwidth bump plus a little architectural efficiency — modest and predictable. Second, the prefill speedup is consistently larger than the generation speedup across every tier, because prefill is the compute-bound phase the Neural Accelerators were built for. The exact prefill multiplier scales with prompt length: a 200-token chat prompt barely notices, while a 16,000-token codebase context can load dramatically faster.
In day-to-day terms: on plain chat with short prompts, an M5 feels like a slightly quicker M4. On a coding assistant fed a large file, or a RAG query with a fat retrieved context, the M5 feels meaningfully snappier — the lag before the model starts answering shrinks the most.
Does It Matter for YOUR Use Case?
The honest answer depends entirely on the shape of your prompts. Here is how the M4-to-M5 jump plays out across the three most common local-LLM workloads:
- Chat & quick Q&A — Short prompts, lots of generation. You live in the bandwidth-bound regime, so you see the modest 15–30% generation gain and little prefill benefit. Nice, not transformative. An M4 is still excellent here.
- Coding assistants — Large files, repository context, and long system prompts mean heavy prefill on every request. This is the M5's sweet spot: the time-to-first-token drops the most, and that latency is exactly what makes a coding assistant feel responsive.
- RAG & long-context — Stuffing thousands of tokens of retrieved documents into each query is prefill-dominated work. The M5's Neural Accelerators chew through that context faster, so the wait before each answer shrinks even when generation length is short.
The throughline: if your prompts are long, the M5 pays off; if your prompts are short, the upgrade is incremental. Memory still trumps everything — no chip generation rescues a model that does not fit in RAM, so size your Unified Memory to your largest model first, then choose the generation.
Should You Upgrade?
Move to M5 if…
Your local AI is coding, agentic workflows, or RAG with big contexts — anything prompt-processing heavy. The Neural Accelerators meaningfully cut time-to-first-token, and that latency is what you feel most. If you are buying new anyway, the M5 is the obvious pick at a given tier and RAM size.
Keep your M4 if…
You mostly chat with short prompts, or you already own a healthy M4 with ample RAM. A 15–30% generation bump rarely justifies replacing a working machine. Spend the money on more Unified Memory or a higher bandwidth tier instead — both move the needle further than the generation gap.
The bigger point holds across both generations: for local LLMs, bandwidth and RAM are the decisive specs, and the chip generation is a secondary multiplier on top. The M5's prefill win is genuinely useful for long-context users and is the strongest reason to pick it — but if you are choosing between a higher-RAM M4 and a lower-RAM M5 at the same price, take the RAM. You can read the full chip-by-chip breakdown on our hardware guide.