Can StarCoder 7B run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder 7B needs ~13.8 GB but RTX 3080 10GB only has 10.0 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) — 13.8 GB, exceeds 10.0 GB available

13.8 GB required10.0 GB available

138% VRAM needed

3.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

51.5 tok/s

TTFT

3756 ms

Safe context

Memory

13.8 GB / 10.0 GB

Offload

30%

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 7B on RTX 3080 10GB
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: 51.5 tok/s decode · 3.8s TTFT (warm) · 129 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.8 GB, but this setup only exposes 10.0 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	A	Runs with offload (needs ~0.1 GB host RAM)	98.0 tok/s	1078 ms	8K
Coding	F	Too heavy	51.5 tok/s	3756 ms	8K
Agentic Coding	F	Too heavy	21.0 tok/s	13390 ms	8K
Reasoning	F	Too heavy	51.5 tok/s	4439 ms	8K
RAG	F	Too heavy	21.0 tok/s	16738 ms	8K

Inference speed

StarCoder 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Tight
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	48.0	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits
RTX 3060 12GB	12 GB	Q4_K_M	30.2	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	11.4	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A75
NVFP4	4	3.9 GB	Medium	A76
Q4_K_M	4	4.3 GB	Medium	A76
Q5_K_M	5	5.0 GB	High	A76
Q6_KBest for your GPU	6	5.7 GB	High	A76
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Upgrade-Optionen

Hardware, die StarCoder 7B gut ausführt

RTX 3060 12GBBestes Preis-Leistungs-Verhältnis

12 GB VRAM (+2)

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

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

ca. $329 MSRP

RTX 5060 Ti 16GBGünstige Wahl

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.65 tok/s Dekodierung

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.

ca. $449 MSRP

RTX 4060 Ti 16GBNVIDIA-Upgrade

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 tok/s Dekodierung

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.

ca. $499 MSRP

Frequently asked questions

Can RTX 3080 10GB run StarCoder 7B?

No, StarCoder 7B requires more memory than RTX 3080 10GB provides.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 13.8 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 3080 10GB?

On RTX 3080 10GB, StarCoder 7B achieves approximately 51.5 tokens per second decode speed with a time-to-first-token of 3756ms using Q4_K_M quantization.

Can RTX 3080 10GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on RTX 3080 10GB receives a F grade with 51.5 tok/s and 8K context.

What context window can StarCoder 7B use on RTX 3080 10GB?

On RTX 3080 10GB, StarCoder 7B can safely use up to 8K 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 3080 10GB?

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 3080 10GB See all hardware for StarCoder 7B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/starcoder-7b-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: