Can StarCoder2 15B run on RTX 4070 Laptop 8GB?

Q: What should I upgrade first if StarCoder2 15B feels slow on RTX 4070 Laptop 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

D40Poor

Estimated from fit model

StarCoder2 15B needs ~9.1 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q2_K quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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.

StarCoder2 15B at Q5_K_M needs 14.0 GB — too much for RTX 4070 Laptop 8GB (8.0 GB). Runs at Q2_K (9.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 14.0 GB, exceeds 8.0 GB available

14.0 GB required8.0 GB available

175% VRAM needed

6.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.6 tok/s

TTFT

41925 ms

Safe context

Memory

14.0 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights10.8 GB

KV Cache1.2 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 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: 4.6 tok/s decode · 41.9s TTFT (warm) · 12 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.1 tok/s	20823 ms	4K
Coding	F	Too heavy	4.6 tok/s	41925 ms	4K
Agentic Coding	F	Too heavy	3.9 tok/s	72697 ms	4K
Reasoning	F	Too heavy	4.6 tok/s	49547 ms	4K
RAG	F	Too heavy	3.9 tok/s	90871 ms	4K

Quantization options

How StarCoder2 15B (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

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs StarCoder2 15B well

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.28.6 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 16GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.21.7 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

RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)896 GB/s (+640)

Makes the model fit on the accelerator instead of staying completely out of reach.59.1 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.

~$749 MSRP

Frequently asked questions

Can RTX 4070 Laptop 8GB run StarCoder2 15B?

Yes, RTX 4070 Laptop 8GB can run StarCoder2 15B at Q2_K quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q5_K_M requires 14.0 GB which exceeds available memory, but at Q2_K it needs only 9.1 GB. Expected decode speed: 17.8 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.0 GB at Q5_K_M quantization. On RTX 4070 Laptop 8GB, it fits at Q2_K using 9.1 GB.

What is the best quantization for StarCoder2 15B?

The recommended quantization is Q5_K_M, but on RTX 4070 Laptop 8GB the best fitting quantization is Q2_K, which uses 9.1 GB.

What speed will StarCoder2 15B run at on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, StarCoder2 15B achieves approximately 17.8 tokens per second decode speed with a time-to-first-token of 10896ms using Q2_K quantization.

Can RTX 4070 Laptop 8GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 4070 Laptop 8GB receives a F grade with 4.6 tok/s and 4K context.

What context window can StarCoder2 15B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, StarCoder2 15B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RTX 4070 Laptop 8GB See all hardware for StarCoder2 15B

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