Can StarCoder2 7B run on RTX 3080 Ti 12GB?

YES — Runs Great

B56Good

Estimated from fit model

StarCoder2 7B needs ~8.4 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: 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) — 8.4 GB, 98.0 tok/s, Runs well

8.4 GB required12.0 GB available

70% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

16K

Memory

8.4 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime2.4 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on RTX 3080 Ti 12GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	98.0 tok/s	1078 ms	16K
Coding	B	Runs well	98.0 tok/s	1976 ms	16K
Agentic Coding	B	Runs well	98.0 tok/s	2873 ms	16K
Reasoning	B	Runs well	98.0 tok/s	2335 ms	16K
RAG	B	Runs well	98.0 tok/s	3592 ms	16K

Inference speed

StarCoder2 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 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	Fits
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
RTX 4070 12GB	12 GB	Q4_K_M	96.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	95.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	95.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	61.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	49.4	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.7	Fits

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 StarCoder2 7B (7B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C48
Q3_K_S	3	3.4 GB	Low	C49
NVFP4	4	3.9 GB	Medium	C50
Q4_K_M	4	4.3 GB	Medium	C50
Q5_K_M	5	5.0 GB	High	C51
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
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

Frequently asked questions

Can RTX 3080 Ti 12GB run StarCoder2 7B?

Yes, RTX 3080 Ti 12GB can run StarCoder2 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 8.4 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 RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, StarCoder2 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 3080 Ti 12GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RTX 3080 Ti 12GB receives a B grade with 98.0 tok/s and 16K context.

What context window can StarCoder2 7B use on RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, 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 RTX 3080 Ti 12GB See all hardware for StarCoder2 7B

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