Qwen2.5 1.5B Instruct needs ~5.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 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
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
1.8M
Memory
5.9 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 | 21.0 tok/s | 5029 ms | 1.6M |
| Coding | C | Runs well | 21.0 tok/s | 9219 ms | 1.8M |
| Agentic Coding | C | Runs well | 21.0 tok/s | 13410 ms | 1.8M |
| Reasoning | C | Runs well | 21.0 tok/s | 10895 ms | 1.8M |
| RAG | C | Runs well | 21.0 tok/s | 16762 ms | 1.8M |
How Qwen2.5 1.5B Instruct (1.5B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C44 |
Q3_K_S | 3 | 0.7 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run Qwen2.5 1.5B Instruct on your machine.
Run
lms load hf-qwen--qwen2-5-1-5b-instruct-gguf && lms server startYes, MacBook Pro M3 Pro 36GB can run Qwen2.5 1.5B Instruct with a C grade (Runs well). Expected decode speed: 21.0 tok/s.
Qwen2.5 1.5B Instruct (1.5B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen2.5 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Qwen2.5 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
For coding workloads, Qwen2.5 1.5B Instruct on MacBook Pro M3 Pro 36GB receives a C grade with 21.0 tok/s and 1.8M context.
On MacBook Pro M3 Pro 36GB, Qwen2.5 1.5B Instruct can safely use up to 1.8M 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-qwen--qwen2-5-1-5b-instruct-gguf-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:
0.8 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 0.9 GB | Medium | C44 |
Q5_K_M | 5 | 1.1 GB | High | C44 |
Q6_K | 6 | 1.2 GB | High | C44 |
Q8_0 | 8 | 1.6 GB | Very High | C44 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C44 |
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.