MacBook Pro M4 32GBOpção econômica
32 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.8.7 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Can StarCoder2 15B run on MacBook Air M4 24GB?
YES — Tight Fit
C47Usable
Estimated — low-sample bucket· few comparable runs
StarCoder2 15B needs ~14.4 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~9 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.4 GB, 8.7 tok/s, Tight fit
14.4 GB required17.3 GB available
83% VRAM used
Fit status
Tight fit
Decode
8.7 tok/s
TTFT
22189 ms
Safe context
42K
Memory
14.4 GB / 17.3 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on MacBook Air M4 24GB
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 | 42K |
| Coding | C | Tight fit | 9.4 tok/s | 20503 ms | 42K |
| Agentic Coding | C | Tight fit | 8.7 tok/s | 32275 ms | 42K |
| Reasoning | C | Tight fit | 8.7 tok/s | 26224 ms | 42K |
| RAG | C | Tight fit | 8.7 tok/s | 40344 ms | 42K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on MacBook Air M4 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 | 8.4 GB | Medium | C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
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 |
Get started
Copy-paste commands to run StarCoder2 15B on your machine.
Run
lms load hf-second-state--starcoder2-15b-gguf && lms server startOpções de upgrade
Hardware que roda bem StarCoder2 15B
Mac mini M4 32GBMelhor custo-benefício
32 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.8.7 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Pro M2 Max 32GBUpgrade Apple
32 GB Unified (+8)400 GB/s (+280)
C
Raises estimated decode speed by about 192%.25.4 tok/s decodificação
Raises estimated decode speed by about 192%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Frequently asked questions
Can MacBook Air M4 24GB run StarCoder2 15B?
Yes, MacBook Air M4 24GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 9.4 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 15B?
The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 15B run at on MacBook Air M4 24GB?
On MacBook Air M4 24GB, StarCoder2 15B achieves approximately 9.4 tokens per second decode speed with a time-to-first-token of 20503ms using Q4_K_M quantization.
Can MacBook Air M4 24GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on MacBook Air M4 24GB receives a C grade with 9.4 tok/s and 42K context.
What context window can StarCoder2 15B use on MacBook Air M4 24GB?
On MacBook Air M4 24GB, StarCoder2 15B can safely use up to 42K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Air M4 24GB as fast as VRAM for StarCoder2 15B?
Not always. MacBook Air M4 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.
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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-15b-gguf-on-m4-air-24gb" 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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