RX 7900 XT 20GBBudget pick
800 GB/s (+527)
B
Raises estimated decode speed by about 149%.69.6 tok/s decode
Raises estimated decode speed by about 149%.
~$899 MSRP
Gemma 2 9B needs ~14.1 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~28 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
28.0 tok/s
TTFT
6907 ms
Safe context
8K
Memory
14.1 GB / 17.3 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 | 28.0 tok/s | 3767 ms | 8K |
| Coding | B | Runs well | 28.0 tok/s | 6907 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~0.6 GB host RAM) | 23.5 tok/s | 11968 ms | 8K |
| Reasoning | B | Runs well | 28.0 tok/s | 8162 ms | 8K |
| RAG | C | Very compromised (needs ~0.6 GB host RAM) | 23.5 tok/s | 14960 ms | 8K |
How Gemma 2 9B (9B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B61 |
Q3_K_S | 3 | 4.4 GB | Low | B62 |
NVFP4 | 4 | 5.0 GB | Medium | B63 |
Q4_K_M | 4 | 5.5 GB | Medium | B63 |
Q5_K_M | 5 | 6.5 GB | High | B64 |
Q6_K | 6 | 7.4 GB | High | B65 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B66 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
Raises estimated decode speed by about 149%.
~$899 MSRP
Raises estimated decode speed by about 209%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Yes, MacBook Pro M4 Pro 24GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 28.0 tok/s.
Gemma 2 9B (9B parameters) requires approximately 14.1 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 M4 Pro 24GB, Gemma 2 9B achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6907ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on MacBook Pro M4 Pro 24GB receives a B grade with 28.0 tok/s and 8K context.
On MacBook Pro M4 Pro 24GB, 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.
Not always. MacBook Pro M4 Pro 24GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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