MacBook Pro M4 Max 36GBOpción económica
36 GB Unified (+4)410 GB/s (+290)
C
Sube la velocidad estimada de decodificación alrededor de un 241%.28 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 241%.
~$2,499 MSRP
Can StarCoder2 15B run on MacBook Pro M4 32GB?
YES — Runs Great
C51Usable
Estimated — low-sample bucket· few comparable runs
StarCoder2 15B needs ~16.4 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q5_K_M quantization, expect ~8 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
Q5_K_M (High quality) — 16.4 GB, 8.2 tok/s, Runs well
16.4 GB required23.0 GB available
71% VRAM used
Fit status
Runs well
Decode
8.2 tok/s
TTFT
23521 ms
Safe context
16K
Memory
16.4 GB / 23.0 GB
Memory breakdown
Weights10.8 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on MacBook Pro 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: 8.2 tok/s decode · 23.5s TTFT (warm) · 21 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 | 8.2 tok/s | 12941 ms | 16K |
| Coding | C | Runs well | 8.2 tok/s | 23725 ms | 16K |
| Agentic Coding | C | Runs well | 8.2 tok/s | 34510 ms | 16K |
| Reasoning | C | Runs well | 8.2 tok/s | 28039 ms | 16K |
| RAG | C | Runs well | 8.2 tok/s | 43137 ms | 16K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C48 |
Q3_K_S | 3 | 7.4 GB | Low | C49 |
NVFP4 | 4 | 8.4 GB | Medium | C49 |
Q4_K_M | 4 | 9.2 GB | Medium | C50 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_K | 6 | 12.3 GB | High | C52 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C51 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run StarCoder2 15B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bigcode/starcoder2-15b" \
--hf-file "starcoder2-15b-Q5_K_M.gguf" \
-c 4096 -ngl 99Opciones de mejora
Hardware que ejecuta bien StarCoder2 15B
MacBook Pro M4 Max 48GBMejor relación calidad-precio
48 GB Unified (+16)546 GB/s (+426)
C
Sube la velocidad estimada de decodificación alrededor de un 300%.32.8 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 300%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 32GB run StarCoder2 15B?
Yes, MacBook Pro M4 32GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 8.2 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 16.4 GB of memory with Q5_K_M quantization.
What is the best quantization for StarCoder2 15B?
The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 15B run at on MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, StarCoder2 15B achieves approximately 8.2 tokens per second decode speed with a time-to-first-token of 23725ms using Q5_K_M quantization.
Can MacBook Pro M4 32GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on MacBook Pro M4 32GB receives a C grade with 8.2 tok/s and 16K context.
What context window can StarCoder2 15B use on MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 32GB as fast as VRAM for StarCoder2 15B?
Not always. MacBook Pro 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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