MacBook Pro M2 Max 32GBBudget pick
32 GB Unified (+8)400 GB/s (+127)
C
Adds memory headroom for longer context windows and future model growth.25.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
starcoder2 15b instruct v0.1 needs ~14.4 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~21 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
Tight fit
Decode
21.2 tok/s
TTFT
9120 ms
Safe context
42K
Memory
14.4 GB / 17.3 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 | 21.2 tok/s | 4975 ms | 42K |
| Coding | C | Tight fit | 21.2 tok/s | 9120 ms | 42K |
| Agentic Coding | C | Tight fit | 21.2 tok/s | 13266 ms | 42K |
| Reasoning | C | Tight fit | 21.2 tok/s | 10778 ms | 42K |
| RAG | C | Tight fit | 21.2 tok/s | 16582 ms | 42K |
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C49 |
Q3_K_S | 3 | 7.4 GB | Low | C50 |
NVFP4 | 4 |
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
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Pro M4 Pro 24GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 21.2 tok/s.
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 14.4 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 M4 Pro 24GB, starcoder2 15b instruct v0.1 achieves approximately 21.2 tokens per second decode speed with a time-to-first-token of 9120ms using Q4_K_M quantization.
For coding workloads, starcoder2 15b instruct v0.1 on MacBook Pro M4 Pro 24GB receives a C grade with 21.2 tok/s and 42K context.
On MacBook Pro M4 Pro 24GB, starcoder2 15b instruct v0.1 can safely use up to 42K 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-m4-pro-24gb" 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_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C50 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Not always. MacBook Pro M4 Pro 24GB 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.