8 GB VRAM (+2)224 GB/s (+32)
B
Adds memory headroom for longer context windows and future model growth.36 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Can Granite Code 3B run on RTX 4050 Laptop 6GB?
YES — With Offload
B67Good
○Estimated from fit model
Granite Code 3B needs ~5.8 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 5.8 GB, 48.0 tok/s, Runs with offload
5.8 GB required6.0 GB available
97% VRAM used
Fit status
Runs with offload
Decode
48.0 tok/s
TTFT
4033 ms
Safe context
8K
Memory
5.8 GB / 6.0 GB
Memory breakdown
Weights1.8 GB
KV Cache2.5 GB
Runtime0.9 GB
Headroom0.6 GB
See how fast it feels
See how fast it feelsGranite Code 3B on RTX 4050 Laptop 6GB
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.
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
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 42.0 tok/s | 2514 ms | 8K |
| Coding | B | Runs with offload | 42.0 tok/s | 4610 ms | 8K |
| Agentic Coding | F | Too heavy | 29.2 tok/s | 9642 ms | 8K |
| Reasoning | B | Runs with offload | 42.0 tok/s | 5448 ms | 8K |
| RAG | F | Too heavy | 29.2 tok/s | 12052 ms | 8K |
Quantization options
How Granite Code 3B (3B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B69 |
Q3_K_S | 3 | 1.5 GB | Low | A70 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Granite Code 3B on your machine.
Run
ollama run granite-code:3bUpgrade options
Hardware that runs Granite Code 3B well
8 GB VRAM (+2)448 GB/s (+256)
A
Adds memory headroom for longer context windows and future model growth.57 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
8 GB VRAM (+2)224 GB/s (+32)
A
Adds memory headroom for longer context windows and future model growth.57 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP