MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+290)
C
Raises estimated decode speed by about 240%.29.6 tok/s decode
Raises estimated decode speed by about 240%.
~$2,499 MSRP
StarCoder2 15B needs ~15.3 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 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
8.7 tok/s
TTFT
22189 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 | 8.7 tok/s | 12103 ms | 87K |
| Coding | C | Runs well | 8.7 tok/s | 22189 ms | 87K |
| Agentic Coding | C | Runs well | 8.7 tok/s | 32275 ms | 87K |
| Reasoning | C | Runs well | 8.7 tok/s | 26224 ms | 87K |
| RAG | C | Runs well | 8.7 tok/s | 40344 ms | 87K |
How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M4 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
Raises estimated decode speed by about 240%.
~$2,499 MSRP
Raises estimated decode speed by about 299%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 32GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 8.7 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 M4 32GB, StarCoder2 15B achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22189ms using Q4_K_M quantization.
For coding workloads, StarCoder2 15B on MacBook Pro M4 32GB receives a C grade with 8.7 tok/s and 87K context.
On MacBook Pro M4 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 M4 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-m4-32gb" 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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