456 GB/s (+256)
C
Raises estimated decode speed by about 69%.51.3 tok/s decode
Raises estimated decode speed by about 69%.
~$249 MSRP
Can starcoder2 7b run on MacBook Pro M1 Pro 16GB?
YES — Runs Great
C52Usable
○Estimated from fit model
starcoder2 7b needs ~7.7 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
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) — 7.7 GB, 30.4 tok/s, Runs well
7.7 GB required11.5 GB available
67% VRAM used
Fit status
Runs well
Decode
30.4 tok/s
TTFT
6359 ms
Safe context
90K
Memory
7.7 GB / 11.5 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsstarcoder2 7b on MacBook Pro M1 Pro 16GB
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: 30.4 tok/s decode · 6.4s TTFT (warm) · 76 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 | 30.4 tok/s | 3469 ms | 90K |
| Coding | C | Runs well | 30.4 tok/s | 6359 ms | 90K |
| Agentic Coding | C | Runs well | 30.4 tok/s | 9249 ms | 90K |
| Reasoning | C | Runs well | 30.4 tok/s | 7515 ms | 90K |
| RAG | C | Runs well | 30.4 tok/s | 11562 ms | 90K |
Quantization options
How starcoder2 7b (7B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 | 3.9 GB | Medium | C50 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run starcoder2 7b on your machine.
Run
lms load hf-quantfactory--starcoder2-7b-gguf && lms server startUpgrade options