aya expanse 8b needs ~13.6 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~40 tok/s.
Operating mode
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
Fit status
Runs well
Decode
39.6 tok/s
TTFT
4885 ms
Safe context
570K
Memory
13.6 GB / 46.1 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 39.6 tok/s | 2665 ms | 570K |
| Coding | C | Runs well | 39.6 tok/s | 4885 ms | 570K |
| Agentic Coding | C | Runs well | 39.6 tok/s | 7106 ms | 570K |
| Reasoning | C | Runs well | 39.6 tok/s | 5773 ms | 570K |
| RAG | C | Runs well | 39.6 tok/s | 8882 ms | 570K |
How aya expanse 8b (8B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C41 |
Q3_K_S | 3 | 3.9 GB | Low | C41 |
NVFP4 | 4 |
Copy-paste commands to run aya expanse 8b on your machine.
Run
lms load hf-bartowski--aya-expanse-8b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 183%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 140%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 64GB can run aya expanse 8b with a C grade (Runs well). Expected decode speed: 39.6 tok/s.
aya expanse 8b (8B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.
The recommended quantization for aya expanse 8b is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 64GB, aya expanse 8b achieves approximately 39.6 tokens per second decode speed with a time-to-first-token of 4885ms using Q4_K_M quantization.
For coding workloads, aya expanse 8b on MacBook Pro M4 Pro 64GB receives a C grade with 39.6 tok/s and 570K context.
On MacBook Pro M4 Pro 64GB, aya expanse 8b can safely use up to 570K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--aya-expanse-8b-gguf-on-m4-pro-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| C42 |
Q4_K_M | 4 | 4.9 GB | Medium | C42 |
Q5_K_M | 5 | 5.8 GB | High | C42 |
Q6_K | 6 | 6.6 GB | High | C42 |
Q8_0 | 8 | 8.6 GB | Very High | C42 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C45 |
Not always. MacBook Pro M4 Pro 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.