Can StarCoder 7B run on RTX 3070 Ti 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder 7B needs ~13.6 GB but RTX 3070 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 13.6 GB, exceeds 8.0 GB available

13.6 GB required8.0 GB available

170% VRAM needed

5.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

25.2 tok/s

TTFT

7687 ms

Safe context

Memory

13.6 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache7.3 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 7B on RTX 3070 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: 25.2 tok/s decode · 7.7s TTFT (warm) · 63 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 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	48.8 tok/s	2166 ms	4K
Coding	F	Too heavy	25.2 tok/s	7687 ms	4K
Agentic Coding	F	Too heavy	15.4 tok/s	18312 ms	4K
Reasoning	F	Too heavy	25.2 tok/s	9084 ms	4K
RAG	F	Too heavy	15.4 tok/s	22890 ms	4K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A77
Q3_K_S	3	3.4 GB	Low	A77
NVFP4	4

Upgrade options

Hardware that runs StarCoder 7B well

RTX 3060 12GBBest value

12 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 tok/s decode

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

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

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.65 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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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.

~$499 MSRP

Frequently asked questions

Can RTX 3070 Ti 8GB run StarCoder 7B?

No, StarCoder 7B requires more memory than RTX 3070 Ti 8GB provides.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

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

What speed will StarCoder 7B run at on RTX 3070 Ti 8GB?

On RTX 3070 Ti 8GB, StarCoder 7B achieves approximately 25.2 tokens per second decode speed with a time-to-first-token of 7687ms using Q4_K_M quantization.

Can RTX 3070 Ti 8GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on RTX 3070 Ti 8GB receives a F grade with 25.2 tok/s and 4K context.

What context window can StarCoder 7B use on RTX 3070 Ti 8GB?

On RTX 3070 Ti 8GB, StarCoder 7B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder 7B feels slow on RTX 3070 Ti 8GB?

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 3070 Ti 8GB See all hardware for StarCoder 7B

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