Can StarCoder 7B run on NVIDIA A2 16GB?

YES — Tight Fit

A74Great

Estimated from fit model

StarCoder 7B needs ~14.4 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 14.4 GB, 36.5 tok/s, Tight fit

14.4 GB required16.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

36.5 tok/s

TTFT

5299 ms

Safe context

Memory

14.4 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder 7B on NVIDIA A2 16GB
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: 36.5 tok/s decode · 5.3s TTFT (warm) · 91 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	A	Runs well	36.5 tok/s	2890 ms	8K
Coding	A	Tight fit	36.5 tok/s	5299 ms	8K
Agentic Coding	F	Too heavy	14.4 tok/s	19554 ms	8K
Reasoning	A	Tight fit	36.5 tok/s	6263 ms	8K
RAG	F	Too heavy	14.4 tok/s	24442 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4

Get started

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

Run

lms load starcoder-7b && lms server start

Your hardware

More models your NVIDIA A2 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	30.5 tok/s
Qwen 3 14B	14B	S	19.7 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run StarCoder 7B?

Yes, NVIDIA A2 16GB can run StarCoder 7B with a A grade (Tight fit). Expected decode speed: 36.5 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

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

What speed will StarCoder 7B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, StarCoder 7B achieves approximately 36.5 tokens per second decode speed with a time-to-first-token of 5299ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on NVIDIA A2 16GB receives a A grade with 36.5 tok/s and 8K context.

What context window can StarCoder 7B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for StarCoder 7B

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