MacBook Pro M4 Pro 64GBBudget pick
64 GB Unified (+16)
B
Raises estimated decode speed by about 57%.21.1 tok/s decode
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Aya Expanse 32B needs ~28.0 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~13 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
13.4 tok/s
TTFT
14479 ms
Safe context
8K
Memory
28.0 GB / 34.6 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 13.4 tok/s | 7898 ms | 8K |
| Coding | B | Runs well | 13.4 tok/s | 14479 ms | 8K |
| Agentic Coding | C | Tight fit | 13.4 tok/s | 21061 ms | 8K |
| Reasoning | B | Runs well | 13.4 tok/s | 17112 ms | 8K |
| RAG | C | Tight fit | 13.4 tok/s | 26326 ms | 8K |
How Aya Expanse 32B (32B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C52 |
Q3_K_S | 3 | 15.7 GB | Low | C54 |
NVFP4 | 4 |
Copy-paste commands to run Aya Expanse 32B on your machine.
Run
ollama run aya-expanse:32bUpgrade options
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 150%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 93%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M3 Max 48GB can run Aya Expanse 32B with a B grade (Runs well). Expected decode speed: 13.4 tok/s.
Aya Expanse 32B (32B parameters) requires approximately 28.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 48GB, Aya Expanse 32B achieves approximately 13.4 tokens per second decode speed with a time-to-first-token of 14479ms using Q4_K_M quantization.
For coding workloads, Aya Expanse 32B on MacBook Pro M3 Max 48GB receives a B grade with 13.4 tok/s and 8K context.
On MacBook Pro M3 Max 48GB, Aya Expanse 32B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/aya-expanse-32b-on-m3-max-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
17.9 GB |
| Medium |
| C55 |
Q4_K_M | 4 | 19.5 GB | Medium | C54 |
Q5_K_M | 5 | 23.0 GB | High | C54 |
Q6_KBest for your GPU | 6 | 26.2 GB | High | C54 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Not always. MacBook Pro M3 Max 48GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.