MacBook Pro M4 Pro 64GBOpção econômica
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.19.4 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Can aya expanse 32b heretic MPOA i1 run on MacBook Pro M4 Pro 48GB?
YES — Tight Fit
C49Usable
Estimated — low-sample bucket· few comparable runs
aya expanse 32b heretic MPOA i1 needs ~29.4 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~19 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 29.4 GB, 19.4 tok/s, Tight fit
29.4 GB required34.6 GB available
85% VRAM used
Fit status
Tight fit
Decode
19.4 tok/s
TTFT
9988 ms
Safe context
38K
Memory
29.4 GB / 34.6 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsaya expanse 32b heretic MPOA i1 on MacBook Pro M4 Pro 48GB
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: 19.4 tok/s decode · 10.0s TTFT (warm) · 49 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 | 19.4 tok/s | 5448 ms | 38K |
| Coding | C | Tight fit | 19.4 tok/s | 9988 ms | 38K |
| Agentic Coding | C | Runs with offload | 19.4 tok/s | 14527 ms | 38K |
| Reasoning | C | Tight fit | 19.4 tok/s | 11803 ms | 38K |
| RAG | C | Runs with offload | 19.4 tok/s | 18159 ms | 38K |
Quantization options
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C46 |
Q3_K_S | 3 | 15.7 GB | Low | C48 |
NVFP4 | 4 | 17.9 GB | Medium | C49 |
Q4_K_M | 4 | 19.5 GB | Medium | C49 |
Q5_K_M | 5 | 23.0 GB | High | C48 |
Q6_KBest for your GPU | 6 | 26.2 GB | High | C48 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
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 startOpções de upgrade
Hardware que roda bem aya expanse 32b heretic MPOA i1
MacBook Pro M3 Max 64GBMelhor custo-benefício
64 GB Unified (+16)400 GB/s (+127)
C
Adds memory headroom for longer context windows and future model growth.12.3 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
MacBook Pro M1 Max 64GBUpgrade Apple
64 GB Unified (+16)400 GB/s (+127)
C
Adds memory headroom for longer context windows and future model growth.11.3 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 Pro 48GB run aya expanse 32b heretic MPOA i1?
Yes, MacBook Pro M4 Pro 48GB can run aya expanse 32b heretic MPOA i1 with a C grade (Tight fit). Expected decode speed: 19.4 tok/s.
How much VRAM does aya expanse 32b heretic MPOA i1 need?
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 29.4 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 Pro 48GB?
On MacBook Pro M4 Pro 48GB, 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.
Can MacBook Pro M4 Pro 48GB run aya expanse 32b heretic MPOA i1 for coding?
For coding workloads, aya expanse 32b heretic MPOA i1 on MacBook Pro M4 Pro 48GB receives a C grade with 19.4 tok/s and 38K context.
What context window can aya expanse 32b heretic MPOA i1 use on MacBook Pro M4 Pro 48GB?
On MacBook Pro M4 Pro 48GB, aya expanse 32b heretic MPOA i1 can safely use up to 38K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for aya expanse 32b heretic MPOA i1?
Not always. MacBook Pro M4 Pro 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.
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