Can Granite Code 3B run on RTX 3000 Ada Laptop 8GB?

YES — Runs Great

A71Great

○Estimated from fit model

Granite Code 3B needs ~6.0 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 6.0 GB, 48.0 tok/s, Runs well

6.0 GB required8.0 GB available

75% VRAM used

Fit status

Runs well

Decode

48.0 tok/s

TTFT

4033 ms

Safe context

Memory

6.0 GB / 8.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGranite Code 3B on RTX 3000 Ada Laptop 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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	48.0 tok/s	2200 ms	8K
Coding	A	Runs well	48.0 tok/s	4033 ms	8K
Agentic Coding	B	Runs with offload (needs ~0.1 GB host RAM)	48.0 tok/s	5867 ms	8K
Reasoning	A	Runs well	48.0 tok/s	4767 ms	8K
RAG	B	Runs with offload (needs ~0.1 GB host RAM)	48.0 tok/s	7333 ms	8K

Quantization options

How Granite Code 3B (3B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	B66
Q3_K_S	3	1.5 GB	Low	B67
NVFP4	4	1.7 GB	Medium	B67
Q4_K_M	4	1.8 GB	Medium	B68
Q5_K_M	5	2.2 GB	High	B68
Q6_K	6	2.5 GB	High	B69
Q8_0Best for your GPU	8	3.2 GB	Very High	B70
F16	16	6.1 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 3B on your machine.

Run

ollama run granite-code:3b

Your hardware

More models your RTX 3000 Ada Laptop 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	A	20.3 tok/s
👁 Alibaba Qwen 3.5 4B	4B	S	64 tok/s
👁 Alibaba Qwen 3 8B	8B	A	26.3 tok/s
👁 Microsoft Phi-4 Mini Reasoning 4B	3.8B	S	60.8 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	27.9 tok/s

Frequently asked questions

See all results for RTX 3000 Ada Laptop 8GB See all hardware for Granite Code 3B

URL: https://willitrunai.com/can-run/granite-code-3b-on-rtx-3000-ada-laptop-8gb

⇱ Granite Code 3B on RTX 3000 Ada Laptop 8GB? YES