Can StarCoder2 3B run on RTX 3000 Ada Laptop 8GB?

YES — Runs Great

C50Usable

Estimated from fit model

StarCoder2 3B needs ~4.2 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 4.2 GB, 42.0 tok/s, Runs well

4.2 GB required8.0 GB available

53% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

190K

Memory

4.2 GB / 8.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsStarCoder2 3B 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	C	Runs well	42.0 tok/s	2514 ms	190K
Coding	C	Runs well	42.0 tok/s	4610 ms	190K
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	190K
Reasoning	C	Runs well	42.0 tok/s	5448 ms	190K
RAG	C	Runs well	42.0 tok/s	8381 ms	190K

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C50
Q3_K_S	3	1.5 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 3B on your machine.

Run

lms load hf-second-state--starcoder2-3b-gguf && lms server start

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run StarCoder2 3B?

Yes, RTX 3000 Ada Laptop 8GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 4.2 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 3B?

The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 3B run at on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, StarCoder2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can RTX 3000 Ada Laptop 8GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on RTX 3000 Ada Laptop 8GB receives a C grade with 42.0 tok/s and 190K context.

What context window can StarCoder2 3B use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, StarCoder2 3B can safely use up to 190K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3000 Ada Laptop 8GB See all hardware for StarCoder2 3B

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