32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 516%.123.8 tok/s decode
Raises estimated decode speed by about 516%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can StarCoder2 15B run on NVIDIA L4 24GB?
YES — Runs Great
C52Usable
○Estimated from fit model
StarCoder2 15B needs ~15.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q5_K_M quantization, expect ~18 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
Q5_K_M (High quality) — 15.6 GB, 20.1 tok/s, Runs well
15.6 GB required24.0 GB available
65% VRAM used
Fit status
Runs well
Decode
20.1 tok/s
TTFT
9630 ms
Safe context
16K
Memory
15.6 GB / 24.0 GB
Memory breakdown
Weights10.8 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on NVIDIA L4 24GB
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: 20.1 tok/s decode · 9.6s TTFT (warm) · 50 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 | 18.4 tok/s | 5734 ms | 16K |
| Coding | C | Runs well | 18.4 tok/s | 10512 ms | 16K |
| Agentic Coding | C | Runs well | 18.4 tok/s | 15291 ms | 16K |
| Reasoning | C | Runs well | 18.4 tok/s | 12424 ms | 16K |
| RAG | C | Runs well | 18.4 tok/s | 19113 ms | 16K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA L4 24GB (24.0 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 |
Get started
Copy-paste commands to run StarCoder2 15B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bigcode/starcoder2-15b" \
--hf-file "starcoder2-15b-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Hardware that runs StarCoder2 15B well
32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 286%.77.6 tok/s decode
Raises estimated decode speed by about 286%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)900 GB/s (+600)
C
Raises estimated decode speed by about 209%.62.2 tok/s decode
Raises estimated decode speed by about 209%.
Adds memory headroom for longer context windows and future model growth.
~$8,999 MSRP