RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+1071)
C
Raises estimated decode speed by about 198%.57.8 tok/s decode
Raises estimated decode speed by about 198%.
~$4,999 MSRP
aya expanse 32b heretic MPOA i1 needs ~31.1 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~19 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
19.4 tok/s
TTFT
9988 ms
Safe context
80K
Memory
31.1 GB / 46.1 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 | C | Runs well | 19.4 tok/s | 5448 ms | 80K |
| Coding | C | Runs well | 19.4 tok/s | 9988 ms | 80K |
| Agentic Coding | C | Runs well | 19.4 tok/s | 14527 ms | 80K |
| Reasoning | C | Runs well | 19.4 tok/s | 11803 ms | 80K |
| RAG | C | Runs well | 19.4 tok/s | 18159 ms | 80K |
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C44 |
Q3_K_S | 3 | 15.7 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run aya expanse 32b heretic MPOA i1 on your machine.
Run
lms load hf-mradermacher--aya-expanse-32b-heretic-mpoa-i1-gguf && lms server startUpgrade options
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 198%.
~$4,999 MSRP
RTX 6000 Ada 48GBBest valueRaises estimated decode speed by about 108%.
~$6,800 MSRP
Yes, MacBook Pro M4 Pro 64GB can run aya expanse 32b heretic MPOA i1 with a C grade (Runs well). Expected decode speed: 19.4 tok/s.
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 31.1 GB of memory with Q4_K_M quantization.
The recommended quantization for aya expanse 32b heretic MPOA i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 64GB, aya expanse 32b heretic MPOA i1 achieves approximately 19.4 tokens per second decode speed with a time-to-first-token of 9988ms using Q4_K_M quantization.
For coding workloads, aya expanse 32b heretic MPOA i1 on MacBook Pro M4 Pro 64GB receives a C grade with 19.4 tok/s and 80K context.
On MacBook Pro M4 Pro 64GB, aya expanse 32b heretic MPOA i1 can safely use up to 80K tokens of context. The model's official context limit is —, 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/hf-mradermacher--aya-expanse-32b-heretic-mpoa-i1-gguf-on-m4-pro-64gb" 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 |
| C46 |
Q4_K_M | 4 | 19.5 GB | Medium | C46 |
Q5_K_M | 5 | 23.0 GB | High | C48 |
Q6_K | 6 | 26.2 GB | High | C48 |
Q8_0Best for your GPU | 8 | 34.2 GB | Very High | C48 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Not always. MacBook Pro M4 Pro 64GB 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.