20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 80%.71.8 tok/s decode
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can StarCoder2 7B run on NVIDIA A2 16GB?
YES — Runs Great
C48Usable
○Estimated from fit model
StarCoder2 7B needs ~7.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 7.6 GB, 39.9 tok/s, Runs well
7.6 GB required16.0 GB available
48% VRAM used
Fit status
Runs well
Decode
39.9 tok/s
TTFT
4854 ms
Safe context
16K
Memory
7.6 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.5 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 7B on NVIDIA A2 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: 39.9 tok/s decode · 4.9s TTFT (warm) · 100 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 | 36.5 tok/s | 2890 ms | 16K |
| Coding | C | Runs well | 36.5 tok/s | 5299 ms | 16K |
| Agentic Coding | C | Runs well | 36.5 tok/s | 7708 ms | 16K |
| Reasoning | C | Runs well | 36.5 tok/s | 6263 ms | 16K |
| RAG | C | Runs well | 36.5 tok/s | 9635 ms | 16K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C46 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
NVFP4 | 4 |
Get started
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load starcoder2-7b && lms server startUpgrade options
Hardware that runs StarCoder2 7B well
24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 146%.98 tok/s decode
Raises estimated decode speed by about 146%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 146%.98 tok/s decode
Raises estimated decode speed by about 146%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP