Can StarCoder2 15B run on RTX 2060 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder2 15B needs ~13.8 GB but RTX 2060 6GB only has 6.0 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

Q5_K_M (High quality) — 13.8 GB, exceeds 6.0 GB available

13.8 GB required6.0 GB available

230% VRAM needed

7.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.0 tok/s

TTFT

65363 ms

Safe context

Memory

13.8 GB / 6.0 GB

Offload

60%

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder2 15B on RTX 2060 6GB
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: 3.0 tok/s decode · 65.4s TTFT (warm) · 7 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 6.0 GB of usable VRAM.

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

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	3.0 tok/s	35653 ms	4K
Coding	F	Too heavy	3.0 tok/s	65363 ms	4K
Agentic Coding	F	Too heavy	3.0 tok/s	95074 ms	4K
Reasoning	F	Too heavy	3.0 tok/s	77247 ms	4K
RAG	F	Too heavy	3.0 tok/s	118842 ms	4K

Inference speed

StarCoder2 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 15B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~124 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	Q5_K_M	123.8	Fits
RTX 4090 24GB	24 GB	Q5_K_M	79.0	Fits
RX 7900 XTX 24GB	24 GB	Q5_K_M	71.3	Fits
RTX 3090 24GB	24 GB	Q5_K_M	67.6	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	64.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	57.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	47.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	45.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	32.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	32.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	24.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	22.7	Fits
RTX 4070 12GB	12 GB	Q5_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.0	Fits
RTX 3060 12GB	12 GB	Q5_K_M	12.5	Heavy offload
RTX 4060 8GB	8 GB	Q5_K_M	4.7	Too big

Estimates for single-stream decoding at Q5_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 15B (15B params) fits at each quantization level on RTX 2060 6GB (6.0 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	8.4 GB	Medium	F0
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Opções de upgrade

Hardware que roda bem StarCoder2 15B

RTX 5060 Ti 16GBOpção econômica

16 GB VRAM (+10)448 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.28.6 tok/s decodificação

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 16GBMelhor custo-benefício

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.21.7 tok/s decodificação

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 2000 Ada 16GBUpgrade NVIDIA

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.22.6 tok/s decodificação

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.

~$625 MSRP

RTX 4090 24GBMaior salto

24 GB VRAM (+18)1008 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.79 tok/s decodificação

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.

~$1,599 MSRP

Frequently asked questions

Can RTX 2060 6GB run StarCoder2 15B?

No, StarCoder2 15B requires more memory than RTX 2060 6GB provides.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 13.8 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on RTX 2060 6GB?

On RTX 2060 6GB, StarCoder2 15B achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 65363ms using Q5_K_M quantization.

Can RTX 2060 6GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 2060 6GB receives a F grade with 3.0 tok/s and 4K context.

What context window can StarCoder2 15B use on RTX 2060 6GB?

On RTX 2060 6GB, StarCoder2 15B can safely use up to 4K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on RTX 2060 6GB?

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 2060 6GB See all hardware for StarCoder2 15B

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