RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl1792 GB/s (+992)
C
Raises estimated decode speed by about 224%.77.1 tok/s Dekodierung
Raises estimated decode speed by about 224%.
ca. $9,999 MSRP
Can aya expanse 32b heretic MPOA i1 run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
C46Usable
Estimated from fit model
aya expanse 32b heretic MPOA i1 needs ~38.0 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 38.0 GB, 23.8 tok/s, Runs well
38.0 GB required92.2 GB available
41% VRAM used
Fit status
Runs well
Decode
23.8 tok/s
TTFT
8145 ms
Safe context
247K
Memory
38.0 GB / 92.2 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsaya expanse 32b heretic MPOA i1 on Mac Studio M2 Ultra 128GB
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: 23.8 tok/s decode · 8.1s TTFT (warm) · 59 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 | 23.8 tok/s | 4442 ms | 247K |
| Coding | C | Runs well | 23.8 tok/s | 8145 ms | 247K |
| Agentic Coding | C | Runs well | 23.8 tok/s | 11847 ms | 247K |
| Reasoning | C | Runs well | 23.8 tok/s | 9625 ms | 247K |
| RAG | C | Runs well | 23.8 tok/s | 14808 ms | 247K |
Quantization options
How aya expanse 32b heretic MPOA i1 (32B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | D40 |
Q3_K_S | 3 | 15.7 GB | Low | C40 |
NVFP4 | 4 | 17.9 GB | Medium | C40 |
Q4_K_M | 4 | 19.5 GB | Medium | C41 |
Q5_K_M | 5 | 23.0 GB | High | C41 |
Q6_K | 6 | 26.2 GB | High | C42 |
Q8_0 | 8 | 34.2 GB | Very High | C43 |
F16Best for your GPU | 16 | 65.6 GB | Maximum | C48 |
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 Mac Studio M2 Ultra 128GB run aya expanse 32b heretic MPOA i1?
Yes, Mac Studio M2 Ultra 128GB can run aya expanse 32b heretic MPOA i1 with a C grade (Runs well). Expected decode speed: 23.8 tok/s.
How much VRAM does aya expanse 32b heretic MPOA i1 need?
aya expanse 32b heretic MPOA i1 (32B parameters) requires approximately 38.0 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 Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, aya expanse 32b heretic MPOA i1 achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8145ms using Q4_K_M quantization.
Can Mac Studio M2 Ultra 128GB run aya expanse 32b heretic MPOA i1 for coding?
For coding workloads, aya expanse 32b heretic MPOA i1 on Mac Studio M2 Ultra 128GB receives a C grade with 23.8 tok/s and 247K context.
What context window can aya expanse 32b heretic MPOA i1 use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, aya expanse 32b heretic MPOA i1 can safely use up to 247K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for aya expanse 32b heretic MPOA i1?
Not always. Mac Studio M2 Ultra 128GB 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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