RTX 4000 Ada 20GBOpción económica20 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.29 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 5080 16GB?
YES — Tight Fit
C54Usable
Estimated from fit model
StarCoder2 15B needs ~14.8 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q5_K_M quantization, expect ~64 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: 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
Q5_K_M (High quality) — 14.8 GB, 64.3 tok/s, Tight fit
14.8 GB required16.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
64.3 tok/s
TTFT
3009 ms
Safe context
16K
Memory
14.8 GB / 16.0 GB
Memory breakdown
Weights10.8 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on RTX 5080 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: 64.3 tok/s decode · 3.0s TTFT (warm) · 161 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 64.3 tok/s | 1641 ms | 16K |
| Coding | C | Tight fit | 64.3 tok/s | 3009 ms | 16K |
| Agentic Coding | C | Runs with offload (needs ~0 GB host RAM) | 48.9 tok/s | 5754 ms | 16K |
| Reasoning | C | Tight fit | 64.3 tok/s | 3556 ms | 16K |
| RAG | C | Runs with offload (needs ~0 GB host RAM) | 48.9 tok/s | 7193 ms | 16K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on RTX 5080 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 99Opciones de mejora
Hardware que ejecuta bien StarCoder2 15B
RTX 4000 Ada 20GBOpción económicaRTX 3090 24GBMejor relación calidad-precio24 GB VRAM (+8)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.67.6 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 (+48)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.79 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,599 MSRP
Frequently asked questions
Can RTX 5080 16GB run StarCoder2 15B?
Yes, RTX 5080 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 64.3 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 14.8 GB of memory with Q5_K_M quantization.
What is the best quantization for StarCoder2 15B?
The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 15B run at on RTX 5080 16GB?
On RTX 5080 16GB, StarCoder2 15B achieves approximately 64.3 tokens per second decode speed with a time-to-first-token of 3009ms using Q5_K_M quantization.
Can RTX 5080 16GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on RTX 5080 16GB receives a C grade with 64.3 tok/s and 16K context.
What context window can StarCoder2 15B use on RTX 5080 16GB?
On RTX 5080 16GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
What should I upgrade first if StarCoder2 15B feels slow on RTX 5080 16GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/starcoder2-15b-on-rtx-5080-16gb" 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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