Can StarCoder2 15B run on NVIDIA A100 40GB?

YES — Runs Great

C52Usable

Estimated from fit model

StarCoder2 15B needs ~17.2 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q5_K_M quantization, expect ~123 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 17.2 GB, 134.7 tok/s, Runs well

17.2 GB required40.0 GB available

43% VRAM used

Fit status

Runs well

Decode

134.7 tok/s

TTFT

1438 ms

Safe context

16K

Memory

17.2 GB / 40.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on NVIDIA A100 40GB
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: 134.7 tok/s decode · 1.4s TTFT (warm) · 337 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	123.4 tok/s	856 ms	16K
Coding	C	Runs well	123.4 tok/s	1569 ms	16K
Agentic Coding	C	Runs well	123.4 tok/s	2283 ms	16K
Reasoning	C	Runs well	123.4 tok/s	1855 ms	16K
RAG	C	Runs well	123.4 tok/s	2853 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C44
Q3_K_S	3	7.4 GB	Low	C45
NVFP4	4

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

Frequently asked questions

Can NVIDIA A100 40GB run StarCoder2 15B?

Yes, NVIDIA A100 40GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 123.4 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 17.2 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 A100 40GB?

On NVIDIA A100 40GB, StarCoder2 15B achieves approximately 123.4 tokens per second decode speed with a time-to-first-token of 1569ms using Q5_K_M quantization.

Can NVIDIA A100 40GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA A100 40GB receives a C grade with 123.4 tok/s and 16K context.

What context window can StarCoder2 15B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, 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 A100 40GB See all hardware for StarCoder2 15B

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