MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+4)150 GB/s (+30)
C
Raises estimated decode speed by about 38%.24.1 tok/s decode
Raises estimated decode speed by about 38%.
~$1,999 MSRP
Aya Expanse 8B needs ~11.2 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~18 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
17.5 tok/s
TTFT
11056 ms
Safe context
8K
Memory
11.2 GB / 23.0 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 | 17.5 tok/s | 6031 ms | 8K |
| Coding | C | Runs well | 17.5 tok/s | 11056 ms | 8K |
| Agentic Coding | C | Runs well | 17.5 tok/s | 16082 ms | 8K |
| Reasoning | C | Runs well | 17.5 tok/s | 13067 ms | 8K |
| RAG | C | Runs well | 17.5 tok/s | 20103 ms | 8K |
How Aya Expanse 8B (8B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C46 |
Q3_K_S | 3 | 3.9 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run Aya Expanse 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "CohereForAI/aya-expanse-8b" \
--hf-file "aya-expanse-8b-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Raises estimated decode speed by about 38%.
~$1,999 MSRP
Raises estimated decode speed by about 254%.
~$2,499 MSRP
Raises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 32GB can run Aya Expanse 8B with a C grade (Runs well). Expected decode speed: 17.5 tok/s.
Aya Expanse 8B (8B parameters) requires approximately 11.2 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 32GB, Aya Expanse 8B achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11056ms using Q4_K_M quantization.
For coding workloads, Aya Expanse 8B on MacBook Pro M4 32GB receives a C grade with 17.5 tok/s and 8K context.
On MacBook Pro M4 32GB, Aya Expanse 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/aya-expanse-8b-on-m4-32gb" 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 |
| C47 |
Q4_K_M | 4 | 4.9 GB | Medium | C47 |
Q5_K_M | 5 | 5.8 GB | High | C48 |
Q6_K | 6 | 6.6 GB | High | C48 |
Q8_0 | 8 | 8.6 GB | Very High | C50 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C51 |
Not always. MacBook Pro M4 32GB 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.