Intel Arc Pro B60 24GBBest value456 GB/s (+256)
B
Raises estimated decode speed by about 89%.47.1 tok/s decode
Raises estimated decode speed by about 89%.
~$599 MSRP
Gemma 2 9B needs ~15.3 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~24 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
24.9 tok/s
TTFT
7787 ms
Safe context
8K
Memory
15.3 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 | B | Runs well | 23.7 tok/s | 4460 ms | 8K |
| Coding | B | Runs well | 23.7 tok/s | 8176 ms | 8K |
| Agentic Coding | B | Tight fit | 23.7 tok/s | 11892 ms | 8K |
| Reasoning | B | Runs well | 23.7 tok/s | 9662 ms | 8K |
| RAG | B | Tight fit | 23.7 tok/s | 14865 ms | 8K |
How Gemma 2 9B (9B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B59 |
Q3_K_S | 3 | 4.4 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
Intel Arc Pro B60 24GBBest valueRaises estimated decode speed by about 89%.
~$599 MSRP
Raises estimated decode speed by about 116%.
~$2,499 MSRP
Yes, MacBook Pro M1 Pro 32GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 23.7 tok/s.
Gemma 2 9B (9B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 32GB, Gemma 2 9B achieves approximately 23.7 tokens per second decode speed with a time-to-first-token of 8176ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on MacBook Pro M1 Pro 32GB receives a B grade with 23.7 tok/s and 8K context.
On MacBook Pro M1 Pro 32GB, Gemma 2 9B 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/gemma-2-9b-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 5.5 GB | Medium | B60 |
Q5_K_M | 5 | 6.5 GB | High | B61 |
Q6_K | 6 | 7.4 GB | High | B62 |
Q8_0 | 8 | 9.6 GB | Very High | B63 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B64 |
Not always. MacBook Pro M1 Pro 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.