Can StarCoder2 7B run on NVIDIA A100 80GB?

YES — Runs Great

C45Usable

Estimated from fit model

StarCoder2 7B needs ~14.0 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~98 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) — 14.0 GB, 98.0 tok/s, Runs well

14.0 GB required80.0 GB available

18% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

16K

Memory

14.0 GB / 80.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsStarCoder2 7B 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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	16K
Coding	C	Runs well	98.0 tok/s	1976 ms	16K
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	16K
Reasoning	C	Runs well	98.0 tok/s	2335 ms	16K
RAG	C	Runs well	98.0 tok/s	3592 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A100 80GB run StarCoder2 7B?

Yes, NVIDIA A100 80GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, StarCoder2 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on NVIDIA A100 80GB receives a C grade with 98.0 tok/s and 16K context.

What context window can StarCoder2 7B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, StarCoder2 7B 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 80GB See all hardware for StarCoder2 7B

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