Can Granite 4.1 8B run on RTX 3050 8GB?

BARELY — Tight on Memory

B63Good

Estimated from fit model

Granite 4.1 8B needs ~9.3 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: 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) — 9.3 GB, 17.7 tok/s, Very compromised (needs ~0.7 GB host RAM)

9.3 GB required8.0 GB available

116% VRAM needed

1.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

17.7 tok/s

TTFT

10935 ms

Safe context

Memory

9.3 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on RTX 3050 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: 17.7 tok/s decode · 10.9s TTFT (warm) · 44 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.

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 0.7 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	A	Runs with offload (needs ~0.1 GB host RAM)	23.8 tok/s	4439 ms	7K
Coding	B	Very compromised (needs ~0.7 GB host RAM)	17.7 tok/s	10935 ms	7K
Agentic Coding	F	Too heavy	10.8 tok/s	25954 ms	7K
Reasoning	B	Very compromised (needs ~0.7 GB host RAM)	17.7 tok/s	12923 ms	7K
RAG	F	Too heavy	10.8 tok/s	32443 ms	7K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A78
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A77
Q4_K_MBest for your GPU	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
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

Opciones de mejora

Hardware que ejecuta bien Granite 4.1 8B

RTX 3060 12GBOpción económica

12 GB VRAM (+4)360 GB/s (+136)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.52.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 195%.

~$329 MSRP

RTX 5060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+8)448 GB/s (+224)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.61.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 246%.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+8)288 GB/s (+64)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.46.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 162%.

~$499 MSRP

Frequently asked questions

Can RTX 3050 8GB run Granite 4.1 8B?

Yes, RTX 3050 8GB can run Granite 4.1 8B with a B grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 17.7 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 9.3 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 3050 8GB?

On RTX 3050 8GB, Granite 4.1 8B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10935ms using Q4_K_M quantization.

Can RTX 3050 8GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on RTX 3050 8GB receives a B grade with 17.7 tok/s and 7K context.

What context window can Granite 4.1 8B use on RTX 3050 8GB?

On RTX 3050 8GB, Granite 4.1 8B can safely use up to 7K 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 3050 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.

See all results for RTX 3050 8GB See all hardware for Granite 4.1 8B

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