Can StarCoder 15B run on RTX 3070 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder 15B needs ~27.4 GB but RTX 3070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: 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

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

4.4 tok/s

TTFT

43577 ms

Safe context

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 3070 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: 4.4 tok/s decode · 43.6s TTFT (warm) · 11 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.4 tok/s	23770 ms	4K
Coding	F	Too heavy	4.4 tok/s	43577 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	63385 ms	4K
Reasoning	F	Too heavy	4.4 tok/s	51501 ms	4K
RAG	F	Too heavy	4.4 tok/s	79232 ms	4K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on RTX 3070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	F0
Q3_K_S	3	7.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs StarCoder 15B well

RTX 5090 32GBBudget pick

32 GB VRAM (+24)1792 GB/s (+1344)

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

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+24)896 GB/s (+448)

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

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+24)576 GB/s (+128)

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

Can RTX 3070 8GB run StarCoder 15B?

No, StarCoder 15B requires more memory than RTX 3070 8GB provides.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 27.4 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder 15B?

The recommended quantization for StarCoder 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder 15B run at on RTX 3070 8GB?

On RTX 3070 8GB, StarCoder 15B achieves approximately 4.4 tokens per second decode speed with a time-to-first-token of 43577ms using Q5_K_M quantization.

Can RTX 3070 8GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on RTX 3070 8GB receives a F grade with 4.4 tok/s and 4K context.

What context window can StarCoder 15B use on RTX 3070 8GB?

On RTX 3070 8GB, StarCoder 15B 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 15B feels slow on RTX 3070 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 8GB See all hardware for StarCoder 15B

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