Can StarCoder2 15B run on NVIDIA A100 80GB?

YES — Runs Great

C47Usable

Estimated from fit model

StarCoder2 15B needs ~20.1 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~187 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

Q4_K_M (Medium quality) — 20.1 GB, 187.2 tok/s, Runs well

20.1 GB required80.0 GB available

25% VRAM used

Fit status

Runs well

Decode

187.2 tok/s

TTFT

1034 ms

Safe context

561K

Memory

20.1 GB / 80.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on NVIDIA A100 80GB
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: 187.2 tok/s decode · 1.0s TTFT (warm) · 468 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	187.2 tok/s	564 ms	561K
Coding	C	Runs well	187.2 tok/s	1034 ms	561K
Agentic Coding	C	Runs well	187.2 tok/s	1504 ms	561K
Reasoning	C	Runs well	187.2 tok/s	1222 ms	561K
RAG	C	Runs well	187.2 tok/s	1880 ms	561K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D40
Q3_K_S	3	7.4 GB	Low	D40
NVFP4	4	8.4 GB	Medium	D40
Q4_K_M	4	9.2 GB	Medium	D40
Q5_K_M	5	10.8 GB	High	C40
Q6_K	6	12.3 GB	High	C40
Q8_0	8	16.1 GB	Very High	C41
F16Best for your GPU	16	30.7 GB	Maximum	C44

Get started

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

Run

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

Frequently asked questions

Can NVIDIA A100 80GB run StarCoder2 15B?

Yes, NVIDIA A100 80GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 187.2 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 20.1 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 NVIDIA A100 80GB?

On NVIDIA A100 80GB, StarCoder2 15B achieves approximately 187.2 tokens per second decode speed with a time-to-first-token of 1034ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA A100 80GB receives a C grade with 187.2 tok/s and 561K context.

What context window can StarCoder2 15B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, StarCoder2 15B can safely use up to 561K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for StarCoder2 15B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-15b-gguf-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

187.2 tok/s

564 ms

561K

Coding

Runs well

187.2 tok/s

1034 ms

561K

Agentic Coding

Runs well

187.2 tok/s

1504 ms

561K

Reasoning

Runs well

187.2 tok/s

1222 ms

561K

RAG

Runs well

187.2 tok/s

1880 ms

561K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D40
Q3_K_S	3	7.4 GB	Low	D40
NVFP4	4	8.4 GB	Medium	D40
Q4_K_M	4	9.2 GB	Medium	D40
Q5_K_M	5	10.8 GB	High	C40
Q6_K	6	12.3 GB	High	C40
Q8_0	8	16.1 GB	Very High	C41
F16Best for your GPU	16	30.7 GB	Maximum	C44

Frequently asked questions

Can NVIDIA A100 80GB run StarCoder2 15B?

Yes, NVIDIA A100 80GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 187.2 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 20.1 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 NVIDIA A100 80GB?

On NVIDIA A100 80GB, StarCoder2 15B achieves approximately 187.2 tokens per second decode speed with a time-to-first-token of 1034ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA A100 80GB receives a C grade with 187.2 tok/s and 561K context.

What context window can StarCoder2 15B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, StarCoder2 15B can safely use up to 561K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.