Will It Run AI

Can StarCoder2 15B run on RTX 3050 Ti Laptop 4GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

StarCoder2 15B needs ~13.6 GB but RTX 3050 Ti Laptop 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity
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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) — 13.6 GB, exceeds 4.0 GB available
13.6 GB required4.0 GB available
340% VRAM needed

9.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.3 tok/s

TTFT

83590 ms

Safe context

4K

Memory

13.6 GB / 4.0 GB

Offload

70%

Memory breakdown

Weights10.8 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsStarCoder2 15B on RTX 3050 Ti Laptop 4GB
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: 2.3 tok/s decode · 83.6s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.6 GB, but this setup only exposes 4.0 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.3 tok/s45594 ms4K
CodingFToo heavy2.3 tok/s83590 ms4K
Agentic CodingFToo heavy2.3 tok/s121585 ms4K
ReasoningFToo heavy2.3 tok/s98788 ms4K
RAGFToo heavy2.3 tok/s151981 ms4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowF0
Q3_K_S
3
7.4 GB
LowF0
NVFP4
4
8.4 GB
MediumF0
Q4_K_M
4
9.2 GB
MediumF0
Q5_K_M
5
10.8 GB
HighF0
Q6_K
6
12.3 GB
HighF0
Q8_0
8
16.1 GB
Very HighF0
F16
16
30.7 GB
MaximumF0

Opciones de mejora

Hardware que ejecuta bien StarCoder2 15B

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+12)448 GB/s (+256)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.28.6 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.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+12)288 GB/s (+96)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.21.7 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.

~$499 MSRP

NVIDIARTX 2000 Ada 16GBMejora NVIDIA
16 GB VRAM (+12)288 GB/s (+96)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.22.6 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.

~$625 MSRP

NVIDIARTX 4090 24GBMayor salto
24 GB VRAM (+20)1008 GB/s (+816)
B
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.79 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.

~$1,599 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run StarCoder2 15B?

No, StarCoder2 15B requires more memory than RTX 3050 Ti Laptop 4GB provides.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 13.6 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 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, StarCoder2 15B achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 83590ms using Q5_K_M quantization.

Can RTX 3050 Ti Laptop 4GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 3050 Ti Laptop 4GB receives a F grade with 2.3 tok/s and 4K context.

What context window can StarCoder2 15B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, StarCoder2 15B can safely use up to 4K 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 3050 Ti Laptop 4GB?

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 RTX 3050 Ti Laptop 4GBSee all hardware for StarCoder2 15B
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