starcoder2 15b instruct v0.1 needs ~13.8 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~11 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.5 GB host RAM)
Decode
10.7 tok/s
TTFT
18013 ms
Safe context
9K
Memory
13.8 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 12.0 tok/s | 8824 ms | 9K |
| Coding | D | Runs with offload | 10.7 tok/s | 18013 ms | 9K |
| Agentic Coding | D | Very compromised | 9.1 tok/s | 30795 ms | 9K |
| Reasoning | D | Runs with offload | 10.7 tok/s | 21288 ms | 9K |
| RAG | D | Very compromised | 9.1 tok/s | 38494 ms | 9K |
How starcoder2 15b instruct v0.1 (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 | C52 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-lmstudio-community--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 410%.
~$2,000 MSRP
Yes, MacBook Pro M3 Pro 18GB can run starcoder2 15b instruct v0.1 with a D grade (Runs with offload). Expected decode speed: 10.7 tok/s.
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.8 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 M3 Pro 18GB, starcoder2 15b instruct v0.1 achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18013ms using Q4_K_M quantization.
For coding workloads, starcoder2 15b instruct v0.1 on MacBook Pro M3 Pro 18GB receives a D grade with 10.7 tok/s and 9K context.
On MacBook Pro M3 Pro 18GB, starcoder2 15b instruct v0.1 can safely use up to 9K 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-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf-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>
Preview:
8.4 GB |
| Medium |
| C51 |
Q4_K_MBest for your GPU | 4 | 9.2 GB | Medium | C51 |
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 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.