Can Granite 3.1 8B run on RTX 2060 Super 8GB?

Q: What should I upgrade first if Granite 3.1 8B feels slow on RTX 2060 Super 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Offload

C46Usable

Estimated from fit model

Granite 3.1 8B needs ~8.5 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 8.5 GB, 41.7 tok/s, Runs with offload (needs ~0.3 GB host RAM)

8.5 GB required8.0 GB available

106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

41.7 tok/s

TTFT

4646 ms

Safe context

12K

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on RTX 2060 Super 8GB
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: 41.7 tok/s decode · 4.6s TTFT (warm) · 104 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	65.8 tok/s	1604 ms	12K
Coding	C	Runs with offload (needs ~0.3 GB host RAM)	41.7 tok/s	4646 ms	12K
Agentic Coding	F	Too heavy	26.5 tok/s	10626 ms	12K
Reasoning	C	Runs with offload (needs ~0.3 GB host RAM)	41.7 tok/s	5491 ms	12K
RAG	F	Too heavy	26.5 tok/s	13282 ms

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B58
Q3_K_S	3	3.9 GB	Low	B58
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

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.60.2 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 44%.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.70.4 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 69%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.53.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 28%.

~$499 MSRP

Frequently asked questions

Can RTX 2060 Super 8GB run Granite 3.1 8B?

Yes, RTX 2060 Super 8GB can run Granite 3.1 8B with a C grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 41.7 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 8.5 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 RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Granite 3.1 8B achieves approximately 41.7 tokens per second decode speed with a time-to-first-token of 4646ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on RTX 2060 Super 8GB receives a C grade with 41.7 tok/s and 12K context.

What context window can Granite 3.1 8B use on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Granite 3.1 8B can safely use up to 12K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 3.1 8B feels slow on RTX 2060 Super 8GB?

See all results for RTX 2060 Super 8GB See all hardware for Granite 3.1 8B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/granite-3.1-8b-on-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: