Can StarCoder2 7B run on RTX 2000 Ada Laptop 8GB?

YES — Runs Great

C54Usable

Estimated from fit model

StarCoder2 7B needs ~6.5 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 6.5 GB, 44.0 tok/s, Runs well

6.5 GB required8.0 GB available

81% VRAM used

Fit status

Runs well

Decode

44.0 tok/s

TTFT

4404 ms

Safe context

16K

Memory

6.5 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on RTX 2000 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: 44.0 tok/s decode · 4.4s TTFT (warm) · 110 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	43.8 tok/s	2413 ms	16K
Coding	C	Runs well	43.8 tok/s	4424 ms	16K
Agentic Coding	C	Tight fit	43.8 tok/s	6434 ms	16K
Reasoning	C	Runs well	43.8 tok/s	5228 ms	16K
RAG	C	Tight fit	43.8 tok/s	8043 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C52
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

RTX 3080 10GBBudget pick

10 GB VRAM (+2)760 GB/s (+504)

Raises estimated decode speed by about 91%.84 tok/s decode

Raises estimated decode speed by about 91%.

Adds memory headroom for longer context windows and future model growth.

~$699 MSRP

RTX 2080 Ti 11GBBest value

11 GB VRAM (+3)616 GB/s (+360)

Raises estimated decode speed by about 123%.98 tok/s decode

Raises estimated decode speed by about 123%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

Can RTX 2000 Ada Laptop 8GB run StarCoder2 7B?

Yes, RTX 2000 Ada Laptop 8GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 43.8 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.5 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, StarCoder2 7B achieves approximately 43.8 tokens per second decode speed with a time-to-first-token of 4424ms using Q4_K_M quantization.

Can RTX 2000 Ada Laptop 8GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RTX 2000 Ada Laptop 8GB receives a C grade with 43.8 tok/s and 16K context.

What context window can StarCoder2 7B use on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RTX 2000 Ada Laptop 8GB See all hardware for StarCoder2 7B

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