24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.71.6 tok/s decode
Raises estimated decode speed by about 133%.
~$1,499 MSRP
Can starcoder2 15b instruct v0.1 run on RTX 4000 Ada 20GB?
YES — Runs Great
C53Usable
○Estimated from fit model
starcoder2 15b instruct v0.1 needs ~14.1 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 14.1 GB, 30.7 tok/s, Runs well
14.1 GB required20.0 GB available
71% VRAM used
Fit status
Runs well
Decode
30.7 tok/s
TTFT
6309 ms
Safe context
70K
Memory
14.1 GB / 20.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsstarcoder2 15b instruct v0.1 on RTX 4000 Ada 20GB
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: 30.7 tok/s decode · 6.3s TTFT (warm) · 77 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 | 30.7 tok/s | 3441 ms | 70K |
| Coding | C | Runs well | 30.7 tok/s | 6309 ms | 70K |
| Agentic Coding | C | Runs well | 30.7 tok/s | 9176 ms | 70K |
| Reasoning | C | Runs well | 30.7 tok/s | 7456 ms | 70K |
| RAG | C | Runs well | 30.7 tok/s | 11470 ms | 70K |
Quantization options
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C47 |
Q3_K_S | 3 | 7.4 GB | Low | C48 |
NVFP4 | 4 | 8.4 GB | Medium | C49 |
Q4_K_M | 4 | 9.2 GB | Medium | C50 |
Q5_K_M | 5 | 10.8 GB | High | C50 |
Q6_K | 6 | 12.3 GB | High | C50 |
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 instruct v0.1 on your machine.
Run
lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server startUpgrade options