Can StarCoder2 7B run on RTX 3060 Ti 8GB?

YES — Runs Great

B55Good

Estimated from fit model

StarCoder2 7B needs ~6.5 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~65 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, 65.4 tok/s, Runs well

6.5 GB required8.0 GB available

81% VRAM used

Fit status

Runs well

Decode

65.4 tok/s

TTFT

2959 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 3060 Ti 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: 65.4 tok/s decode · 3.0s TTFT (warm) · 164 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	B	Runs well	65.4 tok/s	1614 ms	16K
Coding	B	Runs well	65.4 tok/s	2959 ms	16K
Agentic Coding	C	Tight fit	65.4 tok/s	4304 ms	16K
Reasoning	B	Runs well	65.4 tok/s	3497 ms	16K
RAG	C	Tight fit	65.4 tok/s	5380 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on RTX 3060 Ti 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	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C52
Q5_K_MBest for your GPU	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

lms load starcoder2-7b && lms server start

Opções de upgrade

Hardware que roda bem StarCoder2 7B

RTX 3080 10GBOpção econômica

10 GB VRAM (+2)760 GB/s (+312)

Raises estimated decode speed by about 28%.84 tok/s decodificação

Raises estimated decode speed by about 28%.

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

~$699 MSRP

Frequently asked questions

Can RTX 3060 Ti 8GB run StarCoder2 7B?

Yes, RTX 3060 Ti 8GB can run StarCoder2 7B with a B grade (Runs well). Expected decode speed: 65.4 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 3060 Ti 8GB?

On RTX 3060 Ti 8GB, StarCoder2 7B achieves approximately 65.4 tokens per second decode speed with a time-to-first-token of 2959ms using Q4_K_M quantization.

Can RTX 3060 Ti 8GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RTX 3060 Ti 8GB receives a B grade with 65.4 tok/s and 16K context.

What context window can StarCoder2 7B use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 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 3060 Ti 8GB See all hardware for StarCoder2 7B

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