Can StarCoder2 15B run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

StarCoder2 15B needs ~16.9 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 16.9 GB, 50.7 tok/s, Runs well

16.9 GB required48.0 GB available

35% VRAM used

Fit status

Runs well

Decode

50.7 tok/s

TTFT

3820 ms

Safe context

299K

Memory

16.9 GB / 48.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on Quadro RTX 8000 48GB
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: 50.7 tok/s decode · 3.8s TTFT (warm) · 127 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	50.7 tok/s	2084 ms	299K
Coding	C	Runs well	50.7 tok/s	3820 ms	299K
Agentic Coding	C	Runs well	50.7 tok/s	5557 ms	299K
Reasoning	C	Runs well	50.7 tok/s	4515 ms	299K
RAG	C	Runs well	50.7 tok/s	6946 ms	299K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C42
Q3_K_S	3	7.4 GB	Low	C42
NVFP4	4	8.4 GB	Medium	C42
Q4_K_M	4	9.2 GB	Medium	C43
Q5_K_M	5	10.8 GB	High	C43
Q6_K	6	12.3 GB	High	C43
Q8_0	8	16.1 GB	Very High	C45
F16Best for your GPU	16	30.7 GB	Maximum	C48

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

lms load hf-second-state--starcoder2-15b-gguf && lms server start

Opções de upgrade

Hardware que roda bem StarCoder2 15B

Mac Studio M3 Ultra 96GBOpção econômica

96 GB Unified (+48)819 GB/s (+147)

Adds memory headroom for longer context windows and future model growth.60.9 tok/s decodificação

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

~$3,999 MSRP

AMD Instinct MI210 64GBMelhor custo-benefício

64 GB VRAM (+16)1638 GB/s (+966)

Raises estimated decode speed by about 140%.121.7 tok/s decodificação

Raises estimated decode speed by about 140%.

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

~$10,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run StarCoder2 15B?

Yes, Quadro RTX 8000 48GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 50.7 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 16.9 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, StarCoder2 15B achieves approximately 50.7 tokens per second decode speed with a time-to-first-token of 3820ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Quadro RTX 8000 48GB receives a C grade with 50.7 tok/s and 299K context.

What context window can StarCoder2 15B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, StarCoder2 15B can safely use up to 299K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for StarCoder2 15B

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