MacBook Pro M4 Max 36GBコスパ重視
36 GB Unified (+4)410 GB/s (+290)
C
Raises estimated decode speed by about 240%.29.6 tok/sデコード
Raises estimated decode speed by about 240%.
〜$2,499 MSRP
Can starcoder2 15b instruct v0.1 run on Mac mini M4 32GB?
YES — Runs Great
C49Usable
Estimated — low-sample bucket· few comparable runs
starcoder2 15b instruct v0.1 needs ~15.3 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 15.3 GB, 8.7 tok/s, Runs well
15.3 GB required23.0 GB available
67% VRAM used
Fit status
Runs well
Decode
8.7 tok/s
TTFT
22189 ms
Safe context
87K
Memory
15.3 GB / 23.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsstarcoder2 15b instruct v0.1 on Mac mini M4 32GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 8.7 tok/s decode · 22.2s TTFT (warm) · 22 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 8.7 tok/s | 12103 ms | 87K |
| Coding | C | Runs well | 9.4 tok/s | 20503 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 |
Quantization options
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on Mac mini 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 | C49 |
Q6_K | 6 | 12.3 GB | High | C50 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C49 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
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 startアップグレードオプション
starcoder2 15b instruct v0.1を快適に動かすハードウェア
Frequently asked questions
Can Mac mini M4 32GB run starcoder2 15b instruct v0.1?
Yes, Mac mini M4 32GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 9.4 tok/s.
How much VRAM does starcoder2 15b instruct v0.1 need?
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
What is the best quantization for starcoder2 15b instruct v0.1?
The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.
What speed will starcoder2 15b instruct v0.1 run at on Mac mini M4 32GB?
On Mac mini M4 32GB, starcoder2 15b instruct v0.1 achieves approximately 9.4 tokens per second decode speed with a time-to-first-token of 20503ms using Q4_K_M quantization.
Can Mac mini M4 32GB run starcoder2 15b instruct v0.1 for coding?
For coding workloads, starcoder2 15b instruct v0.1 on Mac mini M4 32GB receives a C grade with 9.4 tok/s and 87K context.
What context window can starcoder2 15b instruct v0.1 use on Mac mini M4 32GB?
On Mac mini M4 32GB, starcoder2 15b instruct v0.1 can safely use up to 87K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac mini M4 32GB as fast as VRAM for starcoder2 15b instruct v0.1?
Not always. Mac mini 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.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--starcoder2-15b-instruct-v0-1-gguf-on-m4-mini-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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