OLMo 2 32B needs ~29.5 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~21 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
Tight fit
Decode
20.9 tok/s
TTFT
9248 ms
Safe context
4K
Memory
29.5 GB / 34.6 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 | 20.9 tok/s | 5044 ms | 4K |
| Coding | A | Tight fit | 20.9 tok/s | 9248 ms | 4K |
| Agentic Coding | A | Runs with offload | 20.9 tok/s | 13451 ms | 4K |
| Reasoning | A | Tight fit | 20.9 tok/s | 10929 ms | 4K |
| RAG | A | Runs with offload | 20.9 tok/s | 16814 ms | 4K |
How OLMo 2 32B (32B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | A79 |
Q3_K_S | 3 | 15.7 GB | Low | A81 |
NVFP4 | 4 | 17.9 GB | Medium | A82 |
Q4_K_M | 4 | 19.5 GB | Medium | A81 |
Q5_K_M | 5 | 23.0 GB | High | A81 |
Q6_KBest for your GPU | 6 | 26.2 GB | High | A81 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Copy-paste commands to run OLMo 2 32B on your machine.
Run
lms load OLMo-2-0325-32B-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.6 35B A3B | 35B | S | 29.4 tok/s | |
| Qwen 3.5 35B A3B | 35B | S | 32 tok/s | |
 Kimi Linear 48B A3B | 48B | A | 11.8 tok/s |
Yes, MacBook Pro M4 Pro 48GB can run OLMo 2 32B with a A grade (Tight fit). Expected decode speed: 20.9 tok/s.
OLMo 2 32B (32B parameters) requires approximately 29.5 GB of memory with Q4_K_M quantization.
The recommended quantization for OLMo 2 32B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 48GB, OLMo 2 32B achieves approximately 20.9 tokens per second decode speed with a time-to-first-token of 9248ms using Q4_K_M quantization.
For coding workloads, OLMo 2 32B on MacBook Pro M4 Pro 48GB receives a A grade with 20.9 tok/s and 4K context.
On MacBook Pro M4 Pro 48GB, OLMo 2 32B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 Pro 48GB 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/olmo-2-32b-on-m4-pro-48gb" 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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