Can StarCoder2 7B run on NVIDIA A2 16GB?

YES — Runs Great

C49Usable

Estimated from fit model

StarCoder2 7B needs ~7.9 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 7.9 GB, 36.5 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

36.5 tok/s

TTFT

5299 ms

Safe context

174K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 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	C	Runs well	36.5 tok/s	2890 ms	174K
Coding	C	Runs well	36.5 tok/s	5299 ms	174K
Agentic Coding	C	Runs well	36.5 tok/s	7708 ms	174K
Reasoning	C	Runs well	36.5 tok/s	6263 ms	174K
RAG	C	Runs well	36.5 tok/s	9635 ms	174K

Quantization options

How StarCoder2 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	C47
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4

Get started

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

Run

lms load hf-second-state--starcoder2-7b-gguf && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 80%.65.8 tok/s decode

Raises estimated decode speed by about 80%.

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 168%.98 tok/s decode

Raises estimated decode speed by about 168%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 168%.98 tok/s decode

Raises estimated decode speed by about 168%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run StarCoder2 7B?

Yes, NVIDIA A2 16GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 36.5 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 7.9 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 A2 16GB?

On NVIDIA A2 16GB, StarCoder2 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 StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on NVIDIA A2 16GB receives a C grade with 36.5 tok/s and 174K context.

What context window can StarCoder2 7B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, StarCoder2 7B can safely use up to 174K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for StarCoder2 7B

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