Can Granite Code 3B run on RTX 3050 Ti Laptop 4GB?

Q: What should I upgrade first if Granite Code 3B feels slow on RTX 3050 Ti Laptop 4GB?

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.

NO — Won't Fit

F0Won't run

Estimated from fit model

Granite Code 3B needs ~5.9 GB but RTX 3050 Ti Laptop 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: 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) — 5.9 GB, exceeds 4.0 GB available

5.9 GB required4.0 GB available

148% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

27.1 tok/s

TTFT

7153 ms

Safe context

Memory

5.9 GB / 4.0 GB

Offload

30%

Memory breakdown

Weights1.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 3B on RTX 3050 Ti Laptop 4GB
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: 27.1 tok/s decode · 7.2s TTFT (warm) · 68 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 5.9 GB, but this setup only exposes 4.0 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	B	Very compromised	42.0 tok/s	2514 ms	4K
Coding	F	Too heavy	27.1 tok/s	7153 ms	4K
Agentic Coding	F	Too heavy	13.0 tok/s	21729 ms	4K
Reasoning	F	Too heavy	27.1 tok/s	8454 ms	4K
RAG	F	Too heavy	13.0 tok/s	27162 ms	4K

Quantization options

How Granite Code 3B (3B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A72
Q3_K_S	3	1.5 GB	Low	A71
NVFP4	4

Upgrade options

Hardware that runs Granite Code 3B well

GTX 1660 Super 6GBBudget pick

6 GB VRAM (+2)336 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.42 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.

~$229 MSRP

RTX 3050 8GBBest value

8 GB VRAM (+4)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.42 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.

~$249 MSRP

GTX 1060 6GBNVIDIA upgrade

6 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.42 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.

~$249 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run Granite Code 3B?

No, Granite Code 3B requires more memory than RTX 3050 Ti Laptop 4GB provides.

How much VRAM does Granite Code 3B need?

Granite Code 3B (3B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 3B?

The recommended quantization for Granite Code 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 3B run at on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Granite Code 3B achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7153ms using Q4_K_M quantization.

Can RTX 3050 Ti Laptop 4GB run Granite Code 3B for coding?

For coding workloads, Granite Code 3B on RTX 3050 Ti Laptop 4GB receives a F grade with 27.1 tok/s and 4K context.

What context window can Granite Code 3B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Granite Code 3B 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 3B feels slow on RTX 3050 Ti Laptop 4GB?

See all results for RTX 3050 Ti Laptop 4GB See all hardware for Granite Code 3B

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