starcoder2 15b instruct v0.1 needs ~13.5 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~12 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
2.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.4 GB host RAM)
Decode
12.0 tok/s
TTFT
16180 ms
Safe context
4K
Memory
13.5 GB / 11.5 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 ~0.8 GB host RAM) | 13.1 tok/s | 8076 ms | 4K |
| Coding | D | Very compromised (needs ~1.4 GB host RAM) | 12.0 tok/s | 16180 ms | 4K |
| Agentic Coding | F | Too heavy | 10.3 tok/s | 27324 ms | 4K |
| Reasoning | D | Very compromised (needs ~1.4 GB host RAM) | 12.0 tok/s | 19121 ms | 4K |
| RAG | F | Too heavy | 10.3 tok/s | 34155 ms |
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C52 |
Q3_K_S | 3 | 7.4 GB | Low | C52 |
NVFP4Best for your GPU |
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server startUpgrade options
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 A4500 20GBBiggest leapRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 355%.
~$2,000 MSRP
Yes, MacBook Pro M2 Pro 16GB can run starcoder2 15b instruct v0.1 with a D grade (Very compromised (needs ~1.4 GB host RAM)). Expected decode speed: 12.0 tok/s.
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.5 GB of memory with Q4_K_M quantization.
The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, starcoder2 15b instruct v0.1 achieves approximately 12.0 tokens per second decode speed with a time-to-first-token of 16180ms using Q4_K_M quantization.
For coding workloads, starcoder2 15b instruct v0.1 on MacBook Pro M2 Pro 16GB receives a D grade with 12.0 tok/s and 4K context.
On MacBook Pro M2 Pro 16GB, starcoder2 15b instruct v0.1 can safely use up to 4K 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-bartowski--starcoder2-15b-instruct-v0-1-gguf-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
8.4 GB |
| Medium |
| C51 |
Q4_K_M | 4 | 9.2 GB | Medium | F0 |
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 |
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 M2 Pro 16GB 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.