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

YES — Runs Great

A76Great

Estimated from fit model

Granite Code 8B needs ~9.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 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) — 9.6 GB, 34.4 tok/s, Runs well

9.6 GB required16.0 GB available

60% VRAM used

Fit status

Runs well

Decode

34.4 tok/s

TTFT

5634 ms

Safe context

Memory

9.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite Code 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: 34.4 tok/s decode · 5.6s TTFT (warm) · 86 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	32.0 tok/s	3303 ms	8K
Coding	A	Runs well	32.0 tok/s	6056 ms	8K
Agentic Coding	A	Runs well	32.0 tok/s	8809 ms	8K
Reasoning	A	Runs well	32.0 tok/s	7157 ms	8K
RAG	A	Runs well	32.0 tok/s	11011 ms	8K

Quantization options

How Granite Code 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	A72
Q3_K_S	3	3.9 GB	Low	A73
NVFP4	4	4.5 GB	Medium	A73
Q4_K_M	4	4.9 GB	Medium	A74
Q5_K_M	5	5.8 GB	High	A75
Q6_K	6	6.6 GB	High	A75
Q8_0Best for your GPU	8	8.6 GB	Very High	A77
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run granite-code:8b

Your hardware

More models your NVIDIA A2 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	30.5 tok/s
Qwen 3 14B	14B	S	19.7 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	18.7 tok/s
GPT-OSS 20B	21B	A	17.4 tok/s
Ministral 3 14B	14B	A	19.6 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run Granite Code 8B?

Yes, NVIDIA A2 16GB can run Granite Code 8B with a A grade (Runs well). Expected decode speed: 32.0 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

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

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

On NVIDIA A2 16GB, Granite Code 8B achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.

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

For coding workloads, Granite Code 8B on NVIDIA A2 16GB receives a A grade with 32.0 tok/s and 8K context.

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

On NVIDIA A2 16GB, Granite Code 8B 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 Granite Code 8B

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