Can CodeLlama 13B Instruct run on RTX 2060 6GB?

Q: What should I upgrade first if CodeLlama 13B Instruct feels slow on RTX 2060 6GB?

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

CodeLlama 13B Instruct needs ~21.9 GB but RTX 2060 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: 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) — 21.9 GB, exceeds 6.0 GB available

21.9 GB required6.0 GB available

365% VRAM needed

15.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.6 tok/s

TTFT

53441 ms

Safe context

Memory

21.9 GB / 6.0 GB

Offload

70%

Memory breakdown

Weights7.9 GB

KV Cache12.2 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 13B Instruct on RTX 2060 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: 3.6 tok/s decode · 53.4s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 21.9 GB, but this setup only exposes 6.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	3.6 tok/s	29150 ms	4K
Coding	F	Too heavy	3.6 tok/s	53441 ms	4K
Agentic Coding	F	Too heavy	3.6 tok/s	77732 ms	4K
Reasoning	F	Too heavy	3.6 tok/s	63157 ms	4K
RAG	F	Too heavy	3.6 tok/s	97165 ms	4K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	F0
Q3_K_S	3	6.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

RTX 4000 Ada 20GBBest value

20 GB VRAM (+14)360 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.19.2 tok/s decode

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

Raises estimated decode speed by about 433%.

~$1,250 MSRP

RTX 3090 24GBBudget pick

24 GB VRAM (+18)936 GB/s (+600)

Makes the model fit on the accelerator instead of staying completely out of reach.82.6 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.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+18)1008 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.96.6 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.

~$1,599 MSRP

Frequently asked questions

Can RTX 2060 6GB run CodeLlama 13B Instruct?

No, CodeLlama 13B Instruct requires more memory than RTX 2060 6GB provides.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 21.9 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on RTX 2060 6GB?

On RTX 2060 6GB, CodeLlama 13B Instruct achieves approximately 3.6 tokens per second decode speed with a time-to-first-token of 53441ms using Q4_K_M quantization.

Can RTX 2060 6GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on RTX 2060 6GB receives a F grade with 3.6 tok/s and 4K context.

What context window can CodeLlama 13B Instruct use on RTX 2060 6GB?

On RTX 2060 6GB, CodeLlama 13B Instruct can safely use up to 4K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if CodeLlama 13B Instruct feels slow on RTX 2060 6GB?

See all results for RTX 2060 6GB See all hardware for CodeLlama 13B Instruct

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