Can StarCoder2 15B run on RTX 3080 12GB?

BARELY — Tight on Memory

D36Poor

Estimated from fit model

StarCoder2 15B needs ~14.1 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q5_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 14.1 GB, 36.3 tok/s, Very compromised (needs ~1.6 GB host RAM)

14.1 GB required12.0 GB available

118% VRAM needed

2.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.6 GB host RAM)

Decode

36.3 tok/s

TTFT

5330 ms

Safe context

Memory

14.1 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on RTX 3080 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: 36.3 tok/s decode · 5.3s TTFT (warm) · 91 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 20% 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 1.6 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	C	Very compromised (needs ~1.2 GB host RAM)	39.9 tok/s	2649 ms	4K
Coding	D	Very compromised (needs ~1.6 GB host RAM)	36.3 tok/s	5330 ms	4K
Agentic Coding	F	Too heavy	30.5 tok/s	9232 ms	4K
Reasoning	D	Very compromised (needs ~1.6 GB host RAM)	36.3 tok/s	6299 ms	4K
RAG	F	Too heavy	30.5 tok/s	11540 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 3080 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C54
Q3_K_S	3	7.4 GB	Low	C53
NVFP4Best for your GPU	4	8.4 GB	Medium	C53
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

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

Opciones de mejora

Hardware que ejecuta bien StarCoder2 15B

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.27.1 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

RTX 4060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.22.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

RTX 2000 Ada 16GBMejora NVIDIA

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.22.6 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$625 MSRP

RTX 4090 24GBMayor salto

24 GB VRAM (+12)1008 GB/s (+96)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.80.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 123%.

~$1,599 MSRP

Frequently asked questions

Can RTX 3080 12GB run StarCoder2 15B?

Yes, RTX 3080 12GB can run StarCoder2 15B with a D grade (Very compromised (needs ~1.6 GB host RAM)). Expected decode speed: 36.3 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.1 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 3080 12GB?

On RTX 3080 12GB, StarCoder2 15B achieves approximately 36.3 tokens per second decode speed with a time-to-first-token of 5330ms using Q5_K_M quantization.

Can RTX 3080 12GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 3080 12GB receives a D grade with 36.3 tok/s and 4K context.

What context window can StarCoder2 15B use on RTX 3080 12GB?

On RTX 3080 12GB, 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 3080 12GB?

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.

See all results for RTX 3080 12GB See all hardware for StarCoder2 15B

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