RTX PRO 5000 Blackwell 48GBGünstige Wahl1344 GB/s (+798)
C
Raises estimated decode speed by about 88%.57.8 tok/s Dekodierung
Raises estimated decode speed by about 88%.
ca. $4,999 MSRP
Can aya expanse 32b heretic MPOA i1 run on MacBook Pro M4 Max 64GB?
YES — Runs Great
C51Usable
Estimated from fit model
aya expanse 32b heretic MPOA i1 needs ~31.1 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 31.1 GB, 30.8 tok/s, Runs well
31.1 GB required46.1 GB available
67% VRAM used
Fit status
Runs well
Decode
30.8 tok/s
TTFT
6292 ms
Safe context
80K
Memory
31.1 GB / 46.1 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom6.9 GB
See how fast it feels
See how fast it feelsaya expanse 32b heretic MPOA i1 on MacBook Pro M4 Max 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 30.8 tok/s | 3432 ms | 80K |
| Coding | C | Runs well | 17.6 tok/s | 10986 ms | 80K |
| Agentic Coding | C | Runs well | 30.8 tok/s | 9152 ms | 80K |
| Reasoning | C | Runs well | 30.8 tok/s | 7436 ms | 80K |
| RAG | C | Runs well | 30.8 tok/s | 11441 ms | 80K |
Quantization options
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on MacBook Pro M4 Max 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 | 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 |
Get started
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-Optionen
Hardware, die aya expanse 32b heretic MPOA i1 gut ausführt
Frequently asked questions
Can MacBook Pro M4 Max 64GB run aya expanse 32b heretic MPOA i1?
Yes, MacBook Pro M4 Max 64GB can run aya expanse 32b heretic MPOA i1 with a C grade (Runs well). Expected decode speed: 17.6 tok/s.
How much VRAM does aya expanse 32b heretic MPOA i1 need?
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 31.1 GB of memory with Q4_K_M quantization.
What is the best quantization for aya expanse 32b heretic MPOA i1?
The recommended quantization for aya expanse 32b heretic MPOA i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will aya expanse 32b heretic MPOA i1 run at on MacBook Pro M4 Max 64GB?
On MacBook Pro M4 Max 64GB, aya expanse 32b heretic MPOA i1 achieves approximately 17.6 tokens per second decode speed with a time-to-first-token of 10986ms using Q4_K_M quantization.
Can MacBook Pro M4 Max 64GB run aya expanse 32b heretic MPOA i1 for coding?
For coding workloads, aya expanse 32b heretic MPOA i1 on MacBook Pro M4 Max 64GB receives a C grade with 17.6 tok/s and 80K context.
What context window can aya expanse 32b heretic MPOA i1 use on MacBook Pro M4 Max 64GB?
On MacBook Pro M4 Max 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.
Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for aya expanse 32b heretic MPOA i1?
Not always. MacBook Pro M4 Max 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.
Embed this result▼
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-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: