Can Granite Code 20B run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Granite Code 20B needs ~17.6 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 17.6 GB, exceeds 10.0 GB available

17.6 GB required10.0 GB available

176% VRAM needed

7.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.7 tok/s

TTFT

16542 ms

Safe context

Memory

17.6 GB / 10.0 GB

Offload

40%

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 20B 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: 11.7 tok/s decode · 16.5s TTFT (warm) · 29 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.6 GB, but this setup only exposes 10.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	14.3 tok/s	7393 ms	4K
Coding	F	Too heavy	11.7 tok/s	16542 ms	4K
Agentic Coding	F	Too heavy	8.3 tok/s	34127 ms	4K
Reasoning	F	Too heavy	11.7 tok/s	19550 ms	4K
RAG	F	Too heavy	8.3 tok/s	42659 ms	4K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	F0
Q3_K_S	3	9.8 GB	Low	F0
NVFP4	4	11.2 GB	Medium	F0
Q4_K_M	4	12.2 GB	Medium	F0
Q5_K_M	5	14.4 GB	High	F0
Q6_K	6	16.4 GB	High	F0
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

Upgrade options

Hardware that runs Granite Code 20B well

RTX 5060 Ti 16GBBest value

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.14.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.10.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$499 MSRP

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.24.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,250 MSRP

RTX 4090 24GBBiggest leap

24 GB VRAM (+14)1008 GB/s (+248)

Makes the model fit on the accelerator instead of staying completely out of reach.67.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 3080 10GB run Granite Code 20B?

No, Granite Code 20B requires more memory than RTX 3080 10GB provides.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 17.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on RTX 3080 10GB?

On RTX 3080 10GB, Granite Code 20B achieves approximately 11.7 tokens per second decode speed with a time-to-first-token of 16542ms using Q4_K_M quantization.

Can RTX 3080 10GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on RTX 3080 10GB receives a F grade with 11.7 tok/s and 4K context.

What context window can Granite Code 20B use on RTX 3080 10GB?

On RTX 3080 10GB, Granite Code 20B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on RTX 3080 10GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 3080 10GB See all hardware for Granite Code 20B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/granite-code-20b-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:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

14.3 tok/s

7393 ms

Coding

Too heavy

11.7 tok/s

16542 ms

Agentic Coding

Too heavy

8.3 tok/s

34127 ms

Reasoning

Too heavy

11.7 tok/s

19550 ms

RAG

Too heavy

8.3 tok/s

42659 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

7.8 GB

Low

Q3_K_S

9.8 GB

Low

NVFP4

11.2 GB

Medium

Q4_K_M

12.2 GB

Medium

Q5_K_M

14.4 GB

High

Q6_K

16.4 GB

High

Q8_0

21.4 GB

Very High

F16

41.0 GB

Maximum

Frequently asked questions

Can RTX 3080 10GB run Granite Code 20B?

No, Granite Code 20B requires more memory than RTX 3080 10GB provides.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 17.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on RTX 3080 10GB?

On RTX 3080 10GB, Granite Code 20B achieves approximately 11.7 tokens per second decode speed with a time-to-first-token of 16542ms using Q4_K_M quantization.

Can RTX 3080 10GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on RTX 3080 10GB receives a F grade with 11.7 tok/s and 4K context.

What context window can Granite Code 20B use on RTX 3080 10GB?

On RTX 3080 10GB, Granite Code 20B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on RTX 3080 10GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.