RX 7900 XTX 24GBGünstige Wahl
960 GB/s (+560)
C
Raises estimated decode speed by about 215%.75.5 tok/s Dekodierung
Raises estimated decode speed by about 215%.
ca. $999 MSRP
Can starcoder2 15b i1 run on MacBook Pro M1 Max 32GB?
YES — Runs Great
C51Usable
Estimated from fit model
starcoder2 15b i1 needs ~15.3 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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, 24.0 tok/s, Runs well
15.3 GB required23.0 GB available
67% VRAM used
Fit status
Runs well
Decode
24.0 tok/s
TTFT
8052 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 i1 on MacBook Pro M1 Max 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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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 | 24.0 tok/s | 4392 ms | 87K |
| Coding | C | Runs well | 24.0 tok/s | 8052 ms | 87K |
| Agentic Coding | C | Runs well | 24.0 tok/s | 11712 ms | 87K |
| Reasoning | C | Runs well | 24.0 tok/s | 9516 ms | 87K |
| RAG | C | Runs well | 24.0 tok/s | 14640 ms | 87K |
Quantization options
How starcoder2 15b i1 (15B params) fits at each quantization level on MacBook Pro M1 Max 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 i1 on your machine.
Run
lms load hf-mradermacher--starcoder2-15b-i1-gguf && lms server startUpgrade-Optionen
Hardware, die starcoder2 15b i1 gut ausführt
Frequently asked questions
Can MacBook Pro M1 Max 32GB run starcoder2 15b i1?
Yes, MacBook Pro M1 Max 32GB can run starcoder2 15b i1 with a C grade (Runs well). Expected decode speed: 24.0 tok/s.
How much VRAM does starcoder2 15b i1 need?
starcoder2 15b i1 (15B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
What is the best quantization for starcoder2 15b i1?
The recommended quantization for starcoder2 15b i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will starcoder2 15b i1 run at on MacBook Pro M1 Max 32GB?
On MacBook Pro M1 Max 32GB, starcoder2 15b i1 achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8052ms using Q4_K_M quantization.
Can MacBook Pro M1 Max 32GB run starcoder2 15b i1 for coding?
For coding workloads, starcoder2 15b i1 on MacBook Pro M1 Max 32GB receives a C grade with 24.0 tok/s and 87K context.
What context window can starcoder2 15b i1 use on MacBook Pro M1 Max 32GB?
On MacBook Pro M1 Max 32GB, starcoder2 15b i1 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 MacBook Pro M1 Max 32GB as fast as VRAM for starcoder2 15b i1?
Not always. MacBook Pro M1 Max 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--starcoder2-15b-i1-gguf-on-m1-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: