MacBook Pro M4 Max 48GB经济之选
48 GB Unified (+16)
C
This setup is broadly balanced for this model.95.9 tok/s 解码
~$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 ~97 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 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 | 96.5 tok/s | 1094 ms | 16K |
| Coding | C | Runs well | 96.5 tok/s | 2005 ms | 16K |
| Agentic Coding | C | Runs well | 96.5 tok/s | 2917 ms | 16K |
| Reasoning | C | Runs well | 96.5 tok/s | 2370 ms | 16K |
| RAG | C | Runs well | 96.5 tok/s | 3646 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 | 3.9 GB | Medium | C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C43 |
Q6_K | 6 | 5.7 GB | High | C43 |
Q8_0 | 8 | 7.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C47 |
Get started
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load starcoder2-7b && lms server start升级选项
能流畅运行 StarCoder2 7B 的硬件
Frequently asked questions
Can Radeon AI PRO R9700 32GB run StarCoder2 7B?
Yes, Radeon AI PRO R9700 32GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 96.5 tok/s.
How much VRAM does StarCoder2 7B need?
StarCoder2 7B (7B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 7B?
The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 7B run at on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, StarCoder2 7B achieves approximately 96.5 tokens per second decode speed with a time-to-first-token of 2005ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run StarCoder2 7B for coding?
For coding workloads, StarCoder2 7B on Radeon AI PRO R9700 32GB receives a C grade with 96.5 tok/s and 16K context.
What context window can StarCoder2 7B use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
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