RTX 4000 Ada 20GBOpción económica20 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.30.7 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,250 MSRP
Can StarCoder2 15B run on RTX 4070 Ti Super 16GB?
YES — Tight Fit
C52Usable
Estimated from fit model
StarCoder2 15B needs ~13.4 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~60 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
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) — 13.4 GB, 60.1 tok/s, Tight fit
13.4 GB required16.0 GB available
84% VRAM used
Fit status
Tight fit
Decode
60.1 tok/s
TTFT
3222 ms
Safe context
40K
Memory
13.4 GB / 16.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on RTX 4070 Ti Super 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: 60.1 tok/s decode · 3.2s TTFT (warm) · 150 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 | B | Runs well | 60.1 tok/s | 1757 ms | 40K |
| Coding | C | Tight fit | 60.1 tok/s | 3222 ms | 40K |
| Agentic Coding | C | Tight fit | 60.1 tok/s | 4686 ms | 40K |
| Reasoning | C | Tight fit | 60.1 tok/s | 3808 ms | 40K |
| RAG | C | Tight fit | 60.1 tok/s | 5858 ms | 40K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C50 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 | 8.4 GB | Medium | C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
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
lms load hf-second-state--starcoder2-15b-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien StarCoder2 15B
RTX 4000 Ada 20GBOpción económicaRTX 3090 24GBMejor relación calidad-precio24 GB VRAM (+8)936 GB/s (+264)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.68.3 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIA24 GB VRAM (+8)1008 GB/s (+336)
C
Sube la velocidad estimada de decodificación alrededor de un 42%.85.6 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 42%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,599 MSRP
Frequently asked questions
Can RTX 4070 Ti Super 16GB run StarCoder2 15B?
Yes, RTX 4070 Ti Super 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 60.1 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 15B?
The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 15B run at on RTX 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, StarCoder2 15B achieves approximately 60.1 tokens per second decode speed with a time-to-first-token of 3222ms using Q4_K_M quantization.
Can RTX 4070 Ti Super 16GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on RTX 4070 Ti Super 16GB receives a C grade with 60.1 tok/s and 40K context.
What context window can StarCoder2 15B use on RTX 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, StarCoder2 15B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-15b-gguf-on-rtx-4070-ti-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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