RX 7900 XT 20GB经济之选
20 GB VRAM (+4)800 GB/s (+224)
C
Raises estimated decode speed by about 42%.52.5 tok/s 解码
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can StarCoder2 15B run on Radeon PRO W7700 16GB?
YES — Tight Fit
C51Usable
Estimated from fit model
StarCoder2 15B needs ~13.4 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 13.4 GB, 37.1 tok/s, Tight fit
13.4 GB required16.0 GB available
84% VRAM used
Fit status
Tight fit
Decode
37.1 tok/s
TTFT
5213 ms
Safe context
40K
Memory
13.4 GB / 16.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on Radeon PRO W7700 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: 37.1 tok/s decode · 5.2s TTFT (warm) · 93 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 | 37.1 tok/s | 2843 ms | 40K |
| Coding | C | Tight fit | 37.1 tok/s | 5213 ms | 40K |
| Agentic Coding | C | Tight fit | 37.1 tok/s | 7582 ms | 40K |
| Reasoning | C | Tight fit | 37.1 tok/s | 6160 ms | 40K |
| RAG | C | Tight fit | 37.1 tok/s | 9477 ms | 40K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C50 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 | 8.4 GB | Medium | C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run StarCoder2 15B on your machine.
Run
lms load hf-second-state--starcoder2-15b-gguf && lms server start升级选项
能流畅运行 StarCoder2 15B 的硬件
RX 7900 XTX 24GB最佳性价比
24 GB VRAM (+8)960 GB/s (+384)
C
Raises estimated decode speed by about 104%.75.5 tok/s 解码
Raises estimated decode speed by about 104%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
AMD Instinct MI100 32GBAMD 升级
32 GB VRAM (+16)1228 GB/s (+652)
C
Raises estimated decode speed by about 135%.87.2 tok/s 解码
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
~$11,500 MSRP
Frequently asked questions
Can Radeon PRO W7700 16GB run StarCoder2 15B?
Yes, Radeon PRO W7700 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 37.1 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 13.4 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 Radeon PRO W7700 16GB?
On Radeon PRO W7700 16GB, StarCoder2 15B achieves approximately 37.1 tokens per second decode speed with a time-to-first-token of 5213ms using Q4_K_M quantization.
Can Radeon PRO W7700 16GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on Radeon PRO W7700 16GB receives a C grade with 37.1 tok/s and 40K context.
What context window can StarCoder2 15B use on Radeon PRO W7700 16GB?
On Radeon PRO W7700 16GB, StarCoder2 15B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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-radeon-pro-w7700-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: