RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica1792 GB/s (+992)
C
Raises estimated decode speed by about 242%.83.9 tok/s decodificação
Raises estimated decode speed by about 242%.
~$9,999 MSRP
Can Aya Expanse 32B run on Mac Studio M1 Ultra 128GB?
YES — Runs Great
C51Usable
Estimated from fit model
Aya Expanse 32B needs ~36.7 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~23 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) — 36.7 GB, 24.5 tok/s, Runs well
36.7 GB required92.2 GB available
40% VRAM used
Fit status
Runs well
Decode
24.5 tok/s
TTFT
7898 ms
Safe context
8K
Memory
36.7 GB / 92.2 GB
Memory breakdown
Weights19.5 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsAya Expanse 32B on Mac Studio M1 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: 24.5 tok/s decode · 7.9s TTFT (warm) · 61 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 | 24.5 tok/s | 4308 ms | 8K |
| Coding | C | Runs well | 22.5 tok/s | 8589 ms | 8K |
| Agentic Coding | C | Runs well | 24.5 tok/s | 11488 ms | 8K |
| Reasoning | C | Runs well | 24.5 tok/s | 9334 ms | 8K |
| RAG | C | Runs well | 24.5 tok/s | 14360 ms | 8K |
Quantization options
How Aya Expanse 32B (32B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C45 |
Q3_K_S | 3 | 15.7 GB | Low | C46 |
NVFP4 | 4 | 17.9 GB | Medium | C46 |
Q4_K_M | 4 | 19.5 GB | Medium | C46 |
Q5_K_M | 5 | 23.0 GB | High | C47 |
Q6_K | 6 | 26.2 GB | High | C47 |
Q8_0 | 8 | 34.2 GB | Very High | C49 |
F16Best for your GPU | 16 | 65.6 GB | Maximum | C53 |
Get started
Copy-paste commands to run Aya Expanse 32B on your machine.
Run
ollama run aya-expanse:32bOpções de upgrade
Hardware que roda bem Aya Expanse 32B
Frequently asked questions
Can Mac Studio M1 Ultra 128GB run Aya Expanse 32B?
Yes, Mac Studio M1 Ultra 128GB can run Aya Expanse 32B with a C grade (Runs well). Expected decode speed: 22.5 tok/s.
How much VRAM does Aya Expanse 32B need?
Aya Expanse 32B (32B parameters) requires approximately 36.7 GB of memory with Q4_K_M quantization.
What is the best quantization for Aya Expanse 32B?
The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.
What speed will Aya Expanse 32B run at on Mac Studio M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, Aya Expanse 32B achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8589ms using Q4_K_M quantization.
Can Mac Studio M1 Ultra 128GB run Aya Expanse 32B for coding?
For coding workloads, Aya Expanse 32B on Mac Studio M1 Ultra 128GB receives a C grade with 22.5 tok/s and 8K context.
What context window can Aya Expanse 32B use on Mac Studio M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, Aya Expanse 32B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for Aya Expanse 32B?
Not always. Mac Studio M1 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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