Why Memory Bandwidth Is the Only Spec That Matters
When you run a local LLM on Apple Silicon, the process is almost entirely memory-bound, not compute-bound. Every token generated requires loading a massive slice of model weights from RAM into the GPU's execution units. The faster your chip can shuttle data from memory, the faster it generates tokens.
This is why the GPU core count — which is identical at up to 40 cores on both the M4 Max and M3 Max — matters far less than the memory bandwidth figure. The M4 Max's jump from ~400 GB/s to ~546 GB/s is a 36.5% bandwidth increase, and that translates almost linearly into faster token generation on most model sizes.
According to LLMCheck benchmarks, the M4 Max is approximately 35% faster than the M3 Max for token generation across tested model sizes. This tracks closely with the 36.5% memory bandwidth improvement between the two chips.
Other improvements in the M4 generation — more efficient CPU cores, updated Neural Engine, improved media engines — are largely irrelevant to LLM inference speed. Bandwidth is the bottleneck, full stop.
M4 Max vs M3 Max: Head-to-Head Specs
Here is how the two chips compare on every specification that affects local AI performance:
| Spec | M4 Max | M3 Max |
|---|---|---|
| Memory Bandwidth | ~546 GB/s | ~400 GB/s |
| GPU Cores | Up to 40 | Up to 40 |
| Max Unified Memory | 128 GB | 128 GB |
| Neural Engine | 38 TOPS (4th gen) | 18 TOPS (3rd gen) |
| CPU Cores | Up to 16 (14P + 2E) | Up to 16 (12P + 4E) |
| Process Node | 3nm (2nd gen) | 3nm (1st gen) |
| Model Library Access | Identical | Identical |
| Price Premium | +$400–600 vs M3 Max | Baseline |
One important note: the Neural Engine TOPS figure (38 vs 18) matters primarily for on-device Apple Intelligence tasks and Core ML inference, not for LLM inference via Ollama or LM Studio, which routes workloads through the GPU instead. For the LLM use case, bandwidth dominates.
Real Benchmark Numbers
Enough theory — here is what the difference looks like in practice across three representative models, measured on 128GB configurations using Ollama with Q4_K_M quantization:
| Model | M4 Max (128GB) | M3 Max (128GB) | Difference |
|---|---|---|---|
| Llama 3.3 70B Q4 | ~22 tok/s | ~16 tok/s | +37.5% |
| Qwen 3 32B Q4 | ~38 tok/s | ~28 tok/s | +35.7% |
| Gemma 4 26B-A4B Q4 | ~40 tok/s | ~30 tok/s | +33.3% |
The 33–38% improvement is remarkably consistent across model architectures and sizes. This confirms the bandwidth-limited nature of LLM inference: you are essentially paying for proportionally faster data transfer, and you get it.
In practical terms: on Llama 3.3 70B, the M3 Max generates at roughly human reading speed (~16 tok/s is comfortable to read in real-time). The M4 Max at ~22 tok/s feels noticeably snappier — responses appear faster, and for agentic workflows where the model generates many intermediate steps, the wall-clock time difference adds up significantly over a working day.
What Can Each Chip Actually Run?
This is the key question for buyers: do you get access to better models with the M4 Max? The answer is no — both chips support up to 128GB of Unified Memory and can run the exact same model library.
- Up to 7–9B models — Both chips run these at 100+ tok/s. More than fast enough.
- 13–14B models — Both run comfortably at 60–80+ tok/s. No practical difference.
- 30–35B models (dense) — Both handle these well. M4 Max feels noticeably faster.
- 70B models (dense) — M4 Max at ~22 tok/s vs M3 Max at ~16 tok/s. M4 Max is meaningfully better for daily use.
- 128B MoE models — Both can run these with 128GB RAM. M4 Max is faster, but both are borderline for real-time chat.
The real value of the M4 Max is not unlocking new model tiers — it is making existing large models more comfortable to use daily. At 16 tok/s on a 70B model, you occasionally feel like you are waiting. At 22 tok/s, the wait mostly disappears.
The Verdict: Upgrade or Wait?
Buy the M4 Max if…
You run local LLMs daily for coding, writing, or agentic workflows. The 35% throughput gain is tangible in practice — particularly on 70B models where M3 Max feels just slightly sluggish. If local AI is central to your productivity, the $400–600 premium pays for itself in reduced friction within weeks.
Stick with M3 Max if…
You use local AI occasionally or primarily run models up to ~14B parameters — where both chips perform similarly at 60–80+ tok/s. The M3 Max is still an excellent local AI machine and remains capable of running every model in the LLMCheck leaderboard. Redirect the savings toward a RAM upgrade to 128GB instead.
One important caveat: if you are currently on an M3 Max and the machine is otherwise meeting your needs, upgrading purely for LLM speed is hard to justify at full laptop prices. The upgrade makes more sense when purchasing new. If you already own an M3 Max, wait for M5 Max — the architecture shift there (Neural Accelerators per GPU core, ~600 GB/s bandwidth) represents a much larger generational leap than M3-to-M4.