MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)
C
This setup is broadly balanced for this model.95.9 tok/s decode
~$2,499 MSRP
Can StarCoder2 7B run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C47Usable
○Estimated from fit model
StarCoder2 7B needs ~8.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 8.9 GB, 96.5 tok/s, Runs well
8.9 GB required32.0 GB available
28% VRAM used
Fit status
Runs well
Decode
96.5 tok/s
TTFT
2005 ms
Safe context
16K
Memory
8.9 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsStarCoder2 7B on Radeon AI PRO R9700 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: 96.5 tok/s decode · 2.0s TTFT (warm) · 241 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 88.4 tok/s | 1194 ms | 16K |
| Coding | C | Runs well | 88.4 tok/s | 2189 ms | 16K |
| Agentic Coding | C | Runs well | 88.4 tok/s | 3184 ms | 16K |
| Reasoning | C | Runs well | 88.4 tok/s | 2587 ms | 16K |
| RAG | C | Runs well | 88.4 tok/s | 3981 ms | 16K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C42 |
Q3_K_S | 3 | 3.4 GB | Low | C42 |
NVFP4 | 4 |
Get started
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load starcoder2-7b && lms server startUpgrade options