Qwen 2.5 0.5B needs ~5.3 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~7 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
7.0 tok/s
TTFT
27657 ms
Safe context
131K
Memory
5.3 GB / 25.9 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.0 tok/s | 15086 ms | 131K |
| Coding | C | Runs well | 7.0 tok/s | 27657 ms | 131K |
| Agentic Coding | C | Runs well | 7.0 tok/s | 40229 ms | 131K |
| Reasoning | C | Runs well | 7.0 tok/s | 32686 ms | 131K |
| RAG | C | Runs well | 7.0 tok/s | 50286 ms | 131K |
How Qwen 2.5 0.5B (0.5B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | C46 |
Q3_K_S | 3 | 0.2 GB | Low | C46 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 0.5B on your machine.
Run
ollama run qwen2.5:0.5bYes, MacBook Pro M3 Pro 36GB can run Qwen 2.5 0.5B with a C grade (Runs well). Expected decode speed: 7.0 tok/s.
Qwen 2.5 0.5B (0.5B parameters) requires approximately 5.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 0.5B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Qwen 2.5 0.5B achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27657ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 0.5B on MacBook Pro M3 Pro 36GB receives a C grade with 7.0 tok/s and 131K context.
On MacBook Pro M3 Pro 36GB, Qwen 2.5 0.5B can safely use up to 131K tokens of context. The model's official context limit is 131K, 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/qwen-2.5-0.5b-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:
0.3 GB |
| Medium |
| C46 |
Q4_K_M | 4 | 0.3 GB | Medium | C46 |
Q5_K_M | 5 | 0.4 GB | High | C46 |
Q6_K | 6 | 0.4 GB | High | C46 |
Q8_0 | 8 | 0.5 GB | Very High | C46 |
F16Best for your GPU | 16 | 1.0 GB | Maximum | C46 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.