gemma 2b needs ~6.2 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 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
6914 ms
Safe context
1.4M
Memory
6.2 GB / 25.9 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 | 28.0 tok/s | 3771 ms | 1.4M |
| Coding | C | Runs well | 28.0 tok/s | 6914 ms | 1.4M |
| Agentic Coding | C | Runs well | 28.0 tok/s | 10057 ms | 1.4M |
| Reasoning | C | Runs well | 28.0 tok/s | 8171 ms | 1.4M |
| RAG | C | Runs well | 28.0 tok/s | 12571 ms | 1.4M |
How gemma 2b (2B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.8 GB | Low | C44 |
Q3_K_S | 3 | 1.0 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run gemma 2b on your machine.
Run
lms load hf-google--gemma-2b && lms server startYes, MacBook Pro M3 Pro 36GB can run gemma 2b with a C grade (Runs well). Expected decode speed: 28.0 tok/s.
gemma 2b (2B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 2b is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, gemma 2b achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.
For coding workloads, gemma 2b on MacBook Pro M3 Pro 36GB receives a C grade with 28.0 tok/s and 1.4M context.
On MacBook Pro M3 Pro 36GB, gemma 2b can safely use up to 1.4M 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-google--gemma-2b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
1.1 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 1.2 GB | Medium | C44 |
Q5_K_M | 5 | 1.4 GB | High | C44 |
Q6_K | 6 | 1.6 GB | High | C44 |
Q8_0 | 8 | 2.1 GB | Very High | C44 |
F16Best for your GPU | 16 | 4.1 GB | Maximum | C45 |
Not always. MacBook Pro M3 Pro 36GB 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.