96 GB VRAM (+32)1792 GB/s (+1192)
C
Raises estimated decode speed by about 222%.164.5 tok/s decode
Raises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
Can StarCoder2 15B run on NVIDIA A16 64GB?
YES — Runs Great
C46Usable
○Estimated from fit model
StarCoder2 15B needs ~18.5 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~51 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 18.5 GB, 51.1 tok/s, Runs well
18.5 GB required64.0 GB available
29% VRAM used
Fit status
Runs well
Decode
51.1 tok/s
TTFT
3785 ms
Safe context
430K
Memory
18.5 GB / 64.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on NVIDIA A16 64GB
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: 51.1 tok/s decode · 3.8s TTFT (warm) · 128 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 | 51.1 tok/s | 2065 ms | 430K |
| Coding | C | Runs well | 51.1 tok/s | 3785 ms | 430K |
| Agentic Coding | C | Runs well | 51.1 tok/s | 5506 ms | 430K |
| Reasoning | C | Runs well | 51.1 tok/s | 4473 ms | 430K |
| RAG | C | Runs well | 51.1 tok/s | 6882 ms | 430K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C41 |
Q3_K_S | 3 | 7.4 GB | Low | C41 |
NVFP4 | 4 |
Get started
Copy-paste commands to run StarCoder2 15B on your machine.
Run
lms load hf-second-state--starcoder2-15b-gguf && lms server startUpgrade options
Hardware that runs StarCoder2 15B well
96 GB VRAM (+32)1597 GB/s (+997)
C
Raises estimated decode speed by about 187%.146.6 tok/s decode
Raises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
96 GB VRAM (+32)4000 GB/s (+3400)
C
Raises estimated decode speed by about 311%.210 tok/s decode
Raises estimated decode speed by about 311%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP