Can StarCoder2 15B run on NVIDIA A40 48GB?

YES — Runs Great

C50Usable

Estimated from fit model

StarCoder2 15B needs ~18.0 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q5_K_M quantization, expect ~56 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

Q5_K_M (High quality) — 18.0 GB, 56.0 tok/s, Runs well

18.0 GB required48.0 GB available

38% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3459 ms

Safe context

16K

Memory

18.0 GB / 48.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on NVIDIA A40 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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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	C	Runs well	56.0 tok/s	1887 ms	16K
Coding	C	Runs well	56.0 tok/s	3459 ms	16K
Agentic Coding	C	Runs well	56.0 tok/s	5031 ms	16K
Reasoning	C	Runs well	56.0 tok/s	4088 ms	16K
RAG	C	Runs well	56.0 tok/s	6289 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C43
Q3_K_S	3	7.4 GB	Low	C44
NVFP4	4	8.4 GB	Medium	C44
Q4_K_M	4	9.2 GB	Medium	C44
Q5_K_M	5	10.8 GB	High	C44
Q6_K	6	12.3 GB	High	C45
Q8_0	8	16.1 GB	Very High	C46
F16Best for your GPU	16	30.7 GB	Maximum	C49

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

Opções de upgrade

Hardware que roda bem StarCoder2 15B

AMD Instinct MI210 64GBOpção econômica

64 GB VRAM (+16)1638 GB/s (+942)

Raises estimated decode speed by about 105%.114.8 tok/s decodificação

Raises estimated decode speed by about 105%.

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

~$10,000 MSRP

Frequently asked questions

Can NVIDIA A40 48GB run StarCoder2 15B?

Yes, NVIDIA A40 48GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 18.0 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 NVIDIA A40 48GB?

On NVIDIA A40 48GB, StarCoder2 15B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3459ms using Q5_K_M quantization.

Can NVIDIA A40 48GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA A40 48GB receives a C grade with 56.0 tok/s and 16K context.

What context window can StarCoder2 15B use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, StarCoder2 15B 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 NVIDIA A40 48GB See all hardware for StarCoder2 15B

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