Can StarCoder2 7B run on GTX 1080 8GB?

YES — Runs Great

C54Usable

Estimated from fit model

StarCoder2 7B needs ~6.5 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 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, 48.3 tok/s, Runs well

6.5 GB required8.0 GB available

81% VRAM used

Fit status

Runs well

Decode

48.3 tok/s

TTFT

4011 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 GTX 1080 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: 48.3 tok/s decode · 4.0s TTFT (warm) · 121 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	48.3 tok/s	2188 ms	16K
Coding	C	Runs well	48.3 tok/s	4011 ms	16K
Agentic Coding	C	Tight fit	48.3 tok/s	5834 ms	16K
Reasoning	C	Runs well	48.3 tok/s	4740 ms	16K
RAG	C	Tight fit	48.3 tok/s	7293 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on GTX 1080 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

Upgrade options

Hardware that runs StarCoder2 7B well

RTX 3080 10GBBudget pick

10 GB VRAM (+2)760 GB/s (+440)

Raises estimated decode speed by about 74%.84 tok/s decode

Raises estimated decode speed by about 74%.

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

~$699 MSRP

RTX 2080 Ti 11GBBest value

11 GB VRAM (+3)616 GB/s (+296)

Raises estimated decode speed by about 103%.98 tok/s decode

Raises estimated decode speed by about 103%.

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

~$999 MSRP

Frequently asked questions

Can GTX 1080 8GB run StarCoder2 7B?

Yes, GTX 1080 8GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 48.3 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 GTX 1080 8GB?

On GTX 1080 8GB, StarCoder2 7B achieves approximately 48.3 tokens per second decode speed with a time-to-first-token of 4011ms using Q4_K_M quantization.

Can GTX 1080 8GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on GTX 1080 8GB receives a C grade with 48.3 tok/s and 16K context.

What context window can StarCoder2 7B use on GTX 1080 8GB?

On GTX 1080 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 GTX 1080 8GB See all hardware for StarCoder2 7B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/starcoder2-7b-on-gtx-1080-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

48.3 tok/s

2188 ms

16K

Coding

Runs well

48.3 tok/s

4011 ms

16K

Agentic Coding

Tight fit

48.3 tok/s

5834 ms

16K

Reasoning

Runs well

48.3 tok/s

4740 ms

16K

RAG

Tight fit

48.3 tok/s

7293 ms

16K

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

C52

Q3_K_S

3.4 GB

Low

C53

NVFP4

3.9 GB

Medium

C53

Q4_K_M

4.3 GB

Medium

C52

Q5_K_MBest for your GPU

5.0 GB

High

C52

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Frequently asked questions

Can GTX 1080 8GB run StarCoder2 7B?

Yes, GTX 1080 8GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 48.3 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 GTX 1080 8GB?

On GTX 1080 8GB, StarCoder2 7B achieves approximately 48.3 tokens per second decode speed with a time-to-first-token of 4011ms using Q4_K_M quantization.

Can GTX 1080 8GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on GTX 1080 8GB receives a C grade with 48.3 tok/s and 16K context.

What context window can StarCoder2 7B use on GTX 1080 8GB?

On GTX 1080 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.