Can CodeLlama 13B Instruct run on NVIDIA A2 16GB?

Q: What should I upgrade first if CodeLlama 13B Instruct feels slow on NVIDIA A2 16GB?

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 ~22.9 GB but NVIDIA A2 16GB only has 16.0 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) — 22.9 GB, exceeds 16.0 GB available

22.9 GB required16.0 GB available

143% VRAM needed

6.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.9 tok/s

TTFT

28006 ms

Safe context

Memory

22.9 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 13B Instruct on NVIDIA A2 16GB
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: 6.9 tok/s decode · 28.0s TTFT (warm) · 17 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.9 GB, but this setup only exposes 16.0 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	A	Runs with offload (needs ~0.4 GB host RAM)	13.3 tok/s	7965 ms	7K
Coding	F	Too heavy	6.9 tok/s	28006 ms	7K
Agentic Coding	F	Too heavy	3.0 tok/s	95431 ms	7K
Reasoning	F	Too heavy	6.9 tok/s	33098 ms	7K
RAG	F	Too heavy	3.0 tok/s	119289 ms	7K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A74
Q3_K_S	3	6.4 GB	Low	A75
NVFP4	4

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

RTX 4000 Ada 20GBBest value

20 GB VRAM (+4)360 GB/s (+160)

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 178%.

~$1,250 MSRP

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+736)

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 PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

Makes the model fit on the accelerator instead of staying completely out of reach.71.2 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.

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run CodeLlama 13B Instruct?

No, CodeLlama 13B Instruct requires more memory than NVIDIA A2 16GB provides.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 22.9 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, CodeLlama 13B Instruct achieves approximately 6.9 tokens per second decode speed with a time-to-first-token of 28006ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on NVIDIA A2 16GB receives a F grade with 6.9 tok/s and 7K context.

What context window can CodeLlama 13B Instruct use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, CodeLlama 13B Instruct can safely use up to 7K 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 NVIDIA A2 16GB?

See all results for NVIDIA A2 16GB See all hardware for CodeLlama 13B Instruct

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