Can Granite 4.1 8B run on RTX 3080 10GB?

YES — With Offload

A78Great

Estimated from fit model

Granite 4.1 8B needs ~9.5 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: 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) — 9.5 GB, 112.0 tok/s, Runs with offload

9.5 GB required10.0 GB available

95% VRAM used

Fit status

Runs with offload

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

19K

Memory

9.5 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on RTX 3080 10GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	112.0 tok/s	943 ms	19K
Coding	A	Runs with offload	112.0 tok/s	1729 ms	19K
Agentic Coding	B	Very compromised (needs ~0.8 GB host RAM)	65.4 tok/s	4303 ms	19K
Reasoning	A	Runs with offload	112.0 tok/s	2043 ms	19K
RAG	B	Very compromised (needs ~0.8 GB host RAM)	65.4 tok/s	5379 ms	19K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A76
Q3_K_S	3	3.9 GB	Low	A77
NVFP4	4	4.5 GB	Medium	A77
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A77
Q6_KBest for your GPU	6	6.6 GB	High	A76
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Granite 4.1 8B on your machine.

Run

ollama run granite4.1:8b

Your hardware

More models your RTX 3080 10GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	113.1 tok/s
Nemotron Nano 9B v2	9B	A	82.5 tok/s
CodeGeeX 4 9B	9B	A	115.1 tok/s

Frequently asked questions

Can RTX 3080 10GB run Granite 4.1 8B?

Yes, RTX 3080 10GB can run Granite 4.1 8B with a A grade (Runs with offload). Expected decode speed: 112.0 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 8B?

The recommended quantization for Granite 4.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 8B run at on RTX 3080 10GB?

On RTX 3080 10GB, Granite 4.1 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 3080 10GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on RTX 3080 10GB receives a A grade with 112.0 tok/s and 19K context.

What context window can Granite 4.1 8B use on RTX 3080 10GB?

On RTX 3080 10GB, Granite 4.1 8B can safely use up to 19K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 4.1 8B feels slow on RTX 3080 10GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 3080 10GB See all hardware for Granite 4.1 8B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/granite-4.1-8b-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	112.0 tok/s	943 ms	19K
Coding	A	Runs with offload	112.0 tok/s	1729 ms	19K
Agentic Coding	B	Very compromised (needs ~0.8 GB host RAM)	65.4 tok/s	4303 ms	19K
Reasoning	A	Runs with offload	112.0 tok/s	2043 ms	19K
RAG	B	Very compromised (needs ~0.8 GB host RAM)	65.4 tok/s	5379 ms	19K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

A76

Q3_K_S

3.9 GB

Low

A77

NVFP4

4.5 GB

Medium

A77

Q4_K_M

4.9 GB

Medium

A77

Q5_K_M

5.8 GB

High

A77

Q6_KBest for your GPU

6.6 GB

High

A76

Q8_0

8.6 GB

Very High

F16

16.4 GB

Maximum

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

113.1 tok/s

Nemotron Nano 9B v2

82.5 tok/s

CodeGeeX 4 9B

115.1 tok/s