Can StarCoder 15B run on NVIDIA A2 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder 15B needs ~28.2 GB but NVIDIA A2 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 28.2 GB, exceeds 16.0 GB available

28.2 GB required16.0 GB available

176% VRAM needed

12.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.3 tok/s

TTFT

57971 ms

Safe context

Memory

28.2 GB / 16.0 GB

Offload

40%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 15B 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: 3.3 tok/s decode · 58.0s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 28.2 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.3 tok/s	16811 ms	4K
Coding	F	Too heavy	3.3 tok/s	57971 ms	4K
Agentic Coding	F	Too heavy	2.2 tok/s	127421 ms	4K
Reasoning	F	Too heavy	3.3 tok/s	68512 ms	4K
RAG	F	Too heavy	2.2 tok/s	159276 ms	4K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	A75
Q3_K_S	3	7.4 GB	Low	A76
NVFP4	4	8.4 GB	Medium	A77
Q4_K_M	4	9.2 GB	Medium	A76
Q5_K_M	5	10.8 GB	High	A76
Q6_KBest for your GPU	6	12.3 GB	High	A76
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien StarCoder 15B

RTX 5090 32GBOpción económica

32 GB VRAM (+16)1792 GB/s (+1592)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.113.4 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

32 GB VRAM (+16)896 GB/s (+696)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.71.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

32 GB VRAM (+16)576 GB/s (+376)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.43.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run StarCoder 15B?

No, StarCoder 15B requires more memory than NVIDIA A2 16GB provides.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 28.2 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder 15B?

The recommended quantization for StarCoder 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder 15B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, StarCoder 15B achieves approximately 3.3 tokens per second decode speed with a time-to-first-token of 57971ms using Q5_K_M quantization.

Can NVIDIA A2 16GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on NVIDIA A2 16GB receives a F grade with 3.3 tok/s and 4K context.

What context window can StarCoder 15B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, StarCoder 15B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder 15B feels slow on NVIDIA A2 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for NVIDIA A2 16GB See all hardware for StarCoder 15B

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