Can CodeLlama 7B Instruct run on RTX 4050 Laptop 6GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

CodeLlama 7B Instruct needs ~13.9 GB but RTX 4050 Laptop 6GB only has 6.0 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) — 13.9 GB, exceeds 6.0 GB available

13.9 GB required6.0 GB available

232% VRAM needed

7.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.9 tok/s

TTFT

39320 ms

Safe context

Memory

13.9 GB / 6.0 GB

Offload

60%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 7B Instruct 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: 4.9 tok/s decode · 39.3s TTFT (warm) · 12 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.9 GB, but this setup only exposes 6.0 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	8.4 tok/s	12509 ms	4K
Coding	F	Too heavy	4.9 tok/s	39320 ms	4K
Agentic Coding	F	Too heavy	4.9 tok/s	57193 ms	4K
Reasoning	F	Too heavy	4.9 tok/s	46470 ms	4K
RAG	F	Too heavy	4.9 tok/s	71492 ms	4K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A77
Q3_K_SBest for your GPU	3	3.4 GB	Low	A77

Upgrade options

Hardware that runs CodeLlama 7B Instruct well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+10)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.65 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.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value

16 GB VRAM (+10)288 GB/s (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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.

~$499 MSRP

👁 NVIDIA
RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+10)896 GB/s (+704)

Makes the model fit on the accelerator instead of staying completely out of reach.98 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.

~$749 MSRP

Frequently asked questions

See all results for RTX 4050 Laptop 6GB See all hardware for CodeLlama 7B Instruct

URL: https://willitrunai.com/can-run/codellama-7b-instruct-on-rtx-4050-laptop-6gb

⇱

Can CodeLlama 7B Instruct run on RTX 4050 Laptop 6GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs CodeLlama 7B Instruct well

Frequently asked questions