NVIDIA A100 40GB经济之选40 GB VRAM (+8)1555 GB/s (+915)
C
Raises estimated decode speed by about 246%.142.8 tok/s 解码
Raises estimated decode speed by about 246%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Can starcoder2 15b i1 run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C49Usable
Estimated from fit model
starcoder2 15b i1 needs ~15.0 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 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) — 15.0 GB, 41.3 tok/s, Runs well
15.0 GB required32.0 GB available
47% VRAM used
Fit status
Runs well
Decode
41.3 tok/s
TTFT
4691 ms
Safe context
171K
Memory
15.0 GB / 32.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsstarcoder2 15b i1 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: 41.3 tok/s decode · 4.7s TTFT (warm) · 103 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 | 41.3 tok/s | 2559 ms | 171K |
| Coding | C | Runs well | 41.3 tok/s | 4691 ms | 171K |
| Agentic Coding | C | Runs well | 41.3 tok/s | 6824 ms | 171K |
| Reasoning | C | Runs well | 41.3 tok/s | 5544 ms | 171K |
| RAG | C | Runs well | 41.3 tok/s | 8530 ms | 171K |
Quantization options
How starcoder2 15b i1 (15B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C44 |
Q3_K_S | 3 | 7.4 GB | Low | C44 |
NVFP4 | 4 | 8.4 GB | Medium | C45 |
Q4_K_M | 4 | 9.2 GB | Medium | C45 |
Q5_K_M | 5 | 10.8 GB | High | C46 |
Q6_K | 6 | 12.3 GB | High | C47 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C49 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run starcoder2 15b i1 on your machine.
Run
lms load hf-mradermacher--starcoder2-15b-i1-gguf && lms server start升级选项
能流畅运行 starcoder2 15b i1 的硬件
Frequently asked questions
Can Radeon AI PRO R9700 32GB run starcoder2 15b i1?
Yes, Radeon AI PRO R9700 32GB can run starcoder2 15b i1 with a C grade (Runs well). Expected decode speed: 41.3 tok/s.
How much VRAM does starcoder2 15b i1 need?
starcoder2 15b i1 (15B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
What is the best quantization for starcoder2 15b i1?
The recommended quantization for starcoder2 15b i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will starcoder2 15b i1 run at on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, starcoder2 15b i1 achieves approximately 41.3 tokens per second decode speed with a time-to-first-token of 4691ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run starcoder2 15b i1 for coding?
For coding workloads, starcoder2 15b i1 on Radeon AI PRO R9700 32GB receives a C grade with 41.3 tok/s and 171K context.
What context window can starcoder2 15b i1 use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, starcoder2 15b i1 can safely use up to 171K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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