Can starcoder2 15b instruct v0.1 run on RTX 4070 Laptop 8GB?

Q: What should I upgrade first if starcoder2 15b instruct v0.1 feels slow on RTX 4070 Laptop 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.

NO — Won't Fit

F0Won't run

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~12.9 GB but RTX 4070 Laptop 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

Q4_K_M (Medium quality) — 12.9 GB, exceeds 8.0 GB available

12.9 GB required8.0 GB available

161% VRAM needed

4.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.8 tok/s

TTFT

33232 ms

Safe context

Memory

12.9 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsstarcoder2 15b instruct v0.1 on RTX 4070 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: 5.8 tok/s decode · 33.2s TTFT (warm) · 15 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 12.9 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	6.8 tok/s	15626 ms	4K
Coding	F	Too heavy	5.8 tok/s	33232 ms	4K
Agentic Coding	F	Too heavy	4.5 tok/s	63241 ms	4K
Reasoning	F	Too heavy	5.8 tok/s	39274 ms	4K
RAG	F	Too heavy	4.5 tok/s	79051 ms	4K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 4070 Laptop 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 starcoder2 15b instruct v0.1 well

RTX 3060 12GBBest value

12 GB VRAM (+4)360 GB/s (+104)

Makes the model fit on the accelerator instead of staying completely out of reach.15.7 tok/s decode

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

Raises estimated decode speed by about 171%.

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+8)448 GB/s (+192)

Makes the model fit on the accelerator instead of staying completely out of reach.30.4 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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.23 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 4070 Laptop 8GB run starcoder2 15b instruct v0.1?

No, starcoder2 15b instruct v0.1 requires more memory than RTX 4070 Laptop 8GB provides.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, starcoder2 15b instruct v0.1 achieves approximately 5.8 tokens per second decode speed with a time-to-first-token of 33232ms using Q4_K_M quantization.

Can RTX 4070 Laptop 8GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX 4070 Laptop 8GB receives a F grade with 5.8 tok/s and 4K context.

What context window can starcoder2 15b instruct v0.1 use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, starcoder2 15b instruct v0.1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4070 Laptop 8GB See all hardware for starcoder2 15b instruct v0.1

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