Can Granite 3.1 8B run on NVIDIA A2 16GB?

YES — Runs Great

B56Good

Estimated from fit model

Granite 3.1 8B needs ~9.3 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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) — 9.3 GB, 39.5 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

39.5 tok/s

TTFT

4899 ms

Safe context

71K

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B 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: 39.5 tok/s decode · 4.9s TTFT (warm) · 99 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	B	Runs well	39.5 tok/s	2672 ms	71K
Coding	B	Runs well	36.8 tok/s	5266 ms	71K
Agentic Coding	B	Runs well	39.5 tok/s	7126 ms	71K
Reasoning	B	Runs well	39.5 tok/s	5790 ms	71K
RAG	B	Runs well	39.5 tok/s	8907 ms	71K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C52
Q3_K_S	3	3.9 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Upgrade options

Hardware that runs Granite 3.1 8B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+600)

Raises estimated decode speed by about 184%.112 tok/s decode

Raises estimated decode speed by about 184%.

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

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+440)

Raises estimated decode speed by about 184%.112 tok/s decode

Raises estimated decode speed by about 184%.

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

~$2,000 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run Granite 3.1 8B?

Yes, NVIDIA A2 16GB can run Granite 3.1 8B with a B grade (Runs well). Expected decode speed: 36.8 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Granite 3.1 8B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5266ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on NVIDIA A2 16GB receives a B grade with 36.8 tok/s and 71K context.

What context window can Granite 3.1 8B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Granite 3.1 8B can safely use up to 71K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for Granite 3.1 8B

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