Intel Arc Pro B60 24GBBest value456 GB/s (+256)
C
Raises estimated decode speed by about 89%.26.9 tok/s decode
Raises estimated decode speed by about 89%.
~$599 MSRP
StarCoder2 15B needs ~15.3 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~14 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
14.2 tok/s
TTFT
13626 ms
Safe context
87K
Memory
15.3 GB / 23.0 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 | 14.2 tok/s | 7433 ms | 87K |
| Coding | C | Runs well | 14.2 tok/s | 13626 ms | 87K |
| Agentic Coding | C | Runs well | 14.2 tok/s | 19820 ms | 87K |
| Reasoning | C | Runs well | 14.2 tok/s | 16104 ms | 87K |
| RAG | C | Runs well | 14.2 tok/s | 24775 ms | 87K |
How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C46 |
Q3_K_S | 3 | 7.4 GB | Low | C47 |
NVFP4 | 4 | 8.4 GB | Medium | C48 |
Q4_K_M | 4 | 9.2 GB | Medium | C48 |
Q5_K_M | 5 | 10.8 GB | High | C50 |
Q6_K | 6 | 12.3 GB | High | C50 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C50 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Copy-paste commands to run StarCoder2 15B on your machine.
Run
lms load hf-second-state--starcoder2-15b-gguf && lms server startUpgrade options
Intel Arc Pro B60 24GBBest valueRaises estimated decode speed by about 89%.
~$599 MSRP
Raises estimated decode speed by about 108%.
~$2,499 MSRP
Yes, MacBook Pro M1 Pro 32GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 14.2 tok/s.
StarCoder2 15B (15B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 32GB, StarCoder2 15B achieves approximately 14.2 tokens per second decode speed with a time-to-first-token of 13626ms using Q4_K_M quantization.
For coding workloads, StarCoder2 15B on MacBook Pro M1 Pro 32GB receives a C grade with 14.2 tok/s and 87K context.
On MacBook Pro M1 Pro 32GB, StarCoder2 15B can safely use up to 87K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M1 Pro 32GB 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/hf-second-state--starcoder2-15b-gguf-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: