20 GB VRAM (+4)360 GB/s (+72)
C
Raises estimated decode speed by about 31%.29 tok/s decode
Raises estimated decode speed by about 31%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can StarCoder2 15B run on RTX 4060 Ti 16GB?
YES — Tight Fit
C51Usable
○Estimated from fit model
StarCoder2 15B needs ~14.5 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q5_K_M quantization, expect ~22 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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
Q5_K_M (High quality) — 14.5 GB, 22.2 tok/s, Tight fit
14.5 GB required16.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
22.2 tok/s
TTFT
8733 ms
Safe context
16K
Memory
14.5 GB / 16.0 GB
Memory breakdown
Weights10.8 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on RTX 4060 Ti 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: 22.2 tok/s decode · 8.7s TTFT (warm) · 55 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 | Tight fit | 22.2 tok/s | 4764 ms | 16K |
| Coding | C | Tight fit | 22.2 tok/s | 8733 ms | 16K |
| Agentic Coding | C | Runs with offload | 22.2 tok/s | 12703 ms | 16K |
| Reasoning | C | Tight fit | 22.2 tok/s | 10321 ms | 16K |
| RAG | C | Runs with offload | 22.2 tok/s | 15879 ms | 16K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C51 |
Q3_K_S | 3 | 7.4 GB | Low | C53 |
NVFP4 | 4 | 8.4 GB | Medium | C53 |
Q4_K_M | 4 | 9.2 GB | Medium | C53 |
Q5_K_M | 5 | 10.8 GB | High | C52 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C52 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
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
24 GB VRAM (+8)936 GB/s (+648)
B
Raises estimated decode speed by about 191%.64.5 tok/s decode
Raises estimated decode speed by about 191%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
24 GB VRAM (+8)1008 GB/s (+720)
B
Raises estimated decode speed by about 264%.80.8 tok/s decode
Raises estimated decode speed by about 264%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP