StarCoder2 15B needs ~14.9 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q5_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
1.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.4 GB host RAM)
Decode
9.1 tok/s
TTFT
21256 ms
Safe context
4K
Memory
14.9 GB / 13.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~1 GB host RAM) | 9.6 tok/s | 10962 ms | 4K |
| Coding | D | Very compromised (needs ~1.4 GB host RAM) | 9.1 tok/s | 21256 ms | 4K |
| Agentic Coding | F | Too heavy | 8.2 tok/s | 34152 ms | 4K |
| Reasoning | D | Very compromised (needs ~1.4 GB host RAM) | 9.1 tok/s | 25121 ms | 4K |
| RAG | F | Too heavy | 8.2 tok/s | 42690 ms | 4K |
How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C54 |
Q3_K_S | 3 | 7.4 GB | Low | C53 |
NVFP4 | 4 | 8.4 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 9.2 GB | Medium | C53 |
Q5_K_M | 5 | 10.8 GB | High | F0 |
Q6_K | 6 | 12.3 GB | High | F0 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Copy-paste commands to run StarCoder2 15B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bigcode/starcoder2-15b" \
--hf-file "starcoder2-15b-Q5_K_M.gguf" \
-c 4096 -ngl 99升级选项
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 4090 24GB最大飞跃Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 788%.
~$1,599 MSRP
Yes, MacBook Pro M3 Pro 18GB can run StarCoder2 15B with a D grade (Very compromised (needs ~1.4 GB host RAM)). Expected decode speed: 9.1 tok/s.
StarCoder2 15B (15B parameters) requires approximately 14.9 GB of memory with Q5_K_M quantization.
The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, StarCoder2 15B achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21256ms using Q5_K_M quantization.
For coding workloads, StarCoder2 15B on MacBook Pro M3 Pro 18GB receives a D grade with 9.1 tok/s and 4K context.
On MacBook Pro M3 Pro 18GB, StarCoder2 15B can safely use up to 4K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M3 Pro 18GB 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/starcoder2-15b-on-m3-pro-18gb" 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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