AMD Instinct MI300X 192GBGünstige Wahl
5300 GB/s (+4481)
C
Raises estimated decode speed by about 245%.210 tok/s Dekodierung
Raises estimated decode speed by about 245%.
ca. $15,000 MSRP
Can StarCoder2 15B run on Mac Studio M3 Ultra 256GB?
YES — Runs Great
C45Usable
Estimated from fit model
StarCoder2 15B needs ~39.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~61 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 39.5 GB, 60.9 tok/s, Runs well
39.5 GB required184.3 GB available
21% VRAM used
Fit status
Runs well
Decode
60.9 tok/s
TTFT
3181 ms
Safe context
1.3M
Memory
39.5 GB / 184.3 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom27.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on Mac Studio M3 Ultra 256GB
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: 60.9 tok/s decode · 3.2s TTFT (warm) · 152 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 | 60.9 tok/s | 1735 ms | 1.3M |
| Coding | C | Runs well | 60.9 tok/s | 3181 ms | 1.3M |
| Agentic Coding | C | Runs well | 60.9 tok/s | 4627 ms | 1.3M |
| Reasoning | C | Runs well | 60.9 tok/s | 3759 ms | 1.3M |
| RAG | C | Runs well | 60.9 tok/s | 5783 ms | 1.3M |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | D37 |
Q3_K_S | 3 | 7.4 GB | Low | D37 |
NVFP4 | 4 | 8.4 GB | Medium | D37 |
Q4_K_M | 4 | 9.2 GB | Medium | D37 |
Q5_K_M | 5 | 10.8 GB | High | D37 |
Q6_K | 6 | 12.3 GB | High | D37 |
Q8_0 | 8 | 16.1 GB | Very High | D37 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | D39 |
Get started
Copy-paste commands to run StarCoder2 15B on your machine.
Run
lms load hf-second-state--starcoder2-15b-gguf && lms server startUpgrade-Optionen
Hardware, die StarCoder2 15B gut ausführt
Frequently asked questions
Can Mac Studio M3 Ultra 256GB run StarCoder2 15B?
Yes, Mac Studio M3 Ultra 256GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 60.9 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 39.5 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 Mac Studio M3 Ultra 256GB?
On Mac Studio M3 Ultra 256GB, StarCoder2 15B achieves approximately 60.9 tokens per second decode speed with a time-to-first-token of 3181ms using Q4_K_M quantization.
Can Mac Studio M3 Ultra 256GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on Mac Studio M3 Ultra 256GB receives a C grade with 60.9 tok/s and 1.3M context.
What context window can StarCoder2 15B use on Mac Studio M3 Ultra 256GB?
On Mac Studio M3 Ultra 256GB, StarCoder2 15B can safely use up to 1.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for StarCoder2 15B?
Not always. Mac Studio M3 Ultra 256GB 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-second-state--starcoder2-15b-gguf-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: