VOOZH about

URL: https://willitrunai.com/can-run/starcoder-15b-on-rtx-3000-ada-laptop-8gb

⇱ StarCoder 15B on RTX 3000 Ada Laptop 8GB? No — Alternatives

Can StarCoder 15B run on RTX 3000 Ada Laptop 8GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

StarCoder 15B needs ~27.4 GB but RTX 3000 Ada Laptop 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 27.4 GB, exceeds 8.0 GB available
27.4 GB required8.0 GB available
343% VRAM needed

19.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.0 tok/s

TTFT

65001 ms

Safe context

4K

Memory

27.4 GB / 8.0 GB

Offload

70%

Memory breakdown

Weights10.8 GB
KV Cache14.6 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsStarCoder 15B on RTX 3000 Ada Laptop 8GB
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: 3.0 tok/s decode · 65.0s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 27.4 GB, but this setup only exposes 8.0 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 heavy3.0 tok/s35455 ms4K
CodingFToo heavy3.0 tok/s65001 ms4K
Agentic CodingFToo heavy3.0 tok/s94547 ms4K
ReasoningFToo heavy3.0 tok/s76820 ms4K
RAGFToo heavy3.0 tok/s118184 ms4K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 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

Upgrade options

Hardware that runs StarCoder 15B well

👁 NVIDIA
RTX 5090 32GBBudget pick
32 GB VRAM (+24)1792 GB/s (+1504)
A
Makes the model fit on the accelerator instead of staying completely out of reach.113.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

👁 NVIDIA
RTX PRO 4500 Blackwell 32GBBest value
32 GB VRAM (+24)896 GB/s (+608)
A
Makes the model fit on the accelerator instead of staying completely out of reach.71.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

👁 NVIDIA
RTX 5000 Ada 32GBNVIDIA upgrade
32 GB VRAM (+24)576 GB/s (+288)
A
Makes the model fit on the accelerator instead of staying completely out of reach.43.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,000 MSRP

Frequently asked questions

See all results for RTX 3000 Ada Laptop 8GBSee all hardware for StarCoder 15B