OLMo 2 7B needs ~8.9 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~33 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
35.2 tok/s
TTFT
5493 ms
Safe context
4K
Memory
8.9 GB / 11.5 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 | A | Runs well | 32.8 tok/s | 3221 ms | 4K |
| Coding | A | Runs well | 32.8 tok/s | 5905 ms | 4K |
| Agentic Coding | A | Tight fit | 32.8 tok/s | 8589 ms | 4K |
| Reasoning | A | Runs well | 32.8 tok/s | 6978 ms | 4K |
| RAG | A | Tight fit | 32.8 tok/s | 10736 ms | 4K |
How OLMo 2 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B70 |
Q3_K_S | 3 | 3.4 GB | Low | A71 |
NVFP4 | 4 |
Copy-paste commands to run OLMo 2 7B on your machine.
Run
ollama run olmo2:7bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 27.4 tok/s | |
| Qwen 3 14B | 14B | A | 13.8 tok/s |
Yes, MacBook Pro M2 Pro 16GB can run OLMo 2 7B with a A grade (Runs well). Expected decode speed: 32.8 tok/s.
OLMo 2 7B (7B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
The recommended quantization for OLMo 2 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, OLMo 2 7B achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.
For coding workloads, OLMo 2 7B on MacBook Pro M2 Pro 16GB receives a A grade with 32.8 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, OLMo 2 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/olmo-2-7b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| A72 |
Q4_K_M | 4 | 4.3 GB | Medium | A72 |
Q5_K_M | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | A73 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A72 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| Qwen 3 8B | 8B | S | 30.8 tok/s |
 Nemotron Nano 8B | 8B | S | 30.8 tok/s |
 Ministral 3 14B | 14B | B | 13.7 tok/s |
Not always. MacBook Pro M2 Pro 16GB 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.