Mac mini M4 64GBOpción económica
64 GB Unified (+16)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.8 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,099 MSRP
Can aya expanse 32b heretic MPOA i1 run on MacBook Pro M3 Max 48GB?
YES — Tight Fit
C47Usable
Estimated from fit model
aya expanse 32b heretic MPOA i1 needs ~29.4 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~12 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, 12.3 tok/s, Tight fit
29.4 GB required34.6 GB available
85% VRAM used
Fit status
Tight fit
Decode
12.3 tok/s
TTFT
15746 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 M3 Max 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: 12.3 tok/s decode · 15.7s TTFT (warm) · 31 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 | 12.3 tok/s | 8589 ms | 38K |
| Coding | C | Tight fit | 12.3 tok/s | 15746 ms | 38K |
| Agentic Coding | C | Runs with offload | 12.3 tok/s | 22903 ms | 38K |
| Reasoning | C | Tight fit | 12.3 tok/s | 18609 ms | 38K |
| RAG | C | Runs with offload | 12.3 tok/s | 28629 ms | 38K |
Quantization options
How aya expanse 32b heretic MPOA i1 (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 | 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 startOpciones de mejora
Hardware que ejecuta bien aya expanse 32b heretic MPOA i1
MacBook Pro M4 Pro 64GBMejor relación calidad-precio
64 GB Unified (+16)
C
Sube la velocidad estimada de decodificación alrededor de un 58%.19.4 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 58%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,599 MSRP
MacBook Pro M3 Max 64GBMejora Apple
64 GB Unified (+16)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.12.3 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M3 Max 48GB run aya expanse 32b heretic MPOA i1?
Yes, MacBook Pro M3 Max 48GB can run aya expanse 32b heretic MPOA i1 with a C grade (Tight fit). Expected decode speed: 12.3 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 M3 Max 48GB?
On MacBook Pro M3 Max 48GB, aya expanse 32b heretic MPOA i1 achieves approximately 12.3 tokens per second decode speed with a time-to-first-token of 15746ms using Q4_K_M quantization.
Can MacBook Pro M3 Max 48GB run aya expanse 32b heretic MPOA i1 for coding?
For coding workloads, aya expanse 32b heretic MPOA i1 on MacBook Pro M3 Max 48GB receives a C grade with 12.3 tok/s and 38K context.
What context window can aya expanse 32b heretic MPOA i1 use on MacBook Pro M3 Max 48GB?
On MacBook Pro M3 Max 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 M3 Max 48GB as fast as VRAM for aya expanse 32b heretic MPOA i1?
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.
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