RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
B
Raises estimated decode speed by about 199%.126 tok/s decode
Raises estimated decode speed by about 199%.
~$999 MSRP
Gemma 2 9B needs ~15.3 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~42 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
42.1 tok/s
TTFT
4601 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 | 42.1 tok/s | 2510 ms | 8K |
| Coding | B | Runs well | 42.1 tok/s | 4601 ms | 8K |
| Agentic Coding | B | Tight fit | 42.1 tok/s | 6693 ms | 8K |
| Reasoning | B | Runs well | 42.1 tok/s | 5438 ms | 8K |
| RAG | B | Tight fit | 42.1 tok/s | 8366 ms | 8K |
How Gemma 2 9B (9B params) fits at each quantization level on MacBook Pro M1 Max 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
Raises estimated decode speed by about 199%.
~$999 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 198%.
~$1,499 MSRP
Yes, MacBook Pro M1 Max 32GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 42.1 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 Max 32GB, Gemma 2 9B achieves approximately 42.1 tokens per second decode speed with a time-to-first-token of 4601ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on MacBook Pro M1 Max 32GB receives a B grade with 42.1 tok/s and 8K context.
On MacBook Pro M1 Max 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-max-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 Max 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.