40 GB VRAM (+8)1555 GB/s (+531)
C
Raises estimated decode speed by about 161%.142.8 tok/s decode
Raises estimated decode speed by about 161%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Can starcoder2 15b instruct v0.1 run on AMD Instinct MI60 32GB?
YES — Runs Great
C50Usable
○Estimated from fit model
starcoder2 15b instruct v0.1 needs ~15.0 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~55 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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) — 15.0 GB, 54.8 tok/s, Runs well
15.0 GB required32.0 GB available
47% VRAM used
Fit status
Runs well
Decode
54.8 tok/s
TTFT
3530 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 instruct v0.1 on AMD Instinct MI60 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: 54.8 tok/s decode · 3.5s TTFT (warm) · 137 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 | 54.8 tok/s | 1926 ms | 171K |
| Coding | C | Runs well | 54.8 tok/s | 3530 ms | 171K |
| Agentic Coding | C | Runs well | 54.8 tok/s | 5135 ms | 171K |
| Reasoning | C | Runs well | 54.8 tok/s | 4172 ms | 171K |
| RAG | C | Runs well | 54.8 tok/s | 6419 ms | 171K |
Quantization options
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on AMD Instinct MI60 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 | C48 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf && lms server startUpgrade options