Can CodeLlama 7B Instruct run on RTX 2000 Ada Laptop 8GB?

Q: What should I upgrade first if CodeLlama 7B Instruct feels slow on RTX 2000 Ada Laptop 8GB?

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 7B Instruct needs ~14.1 GB but RTX 2000 Ada Laptop 8GB only has 8.0 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) — 14.1 GB, exceeds 8.0 GB available

14.1 GB required8.0 GB available

176% VRAM needed

6.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.0 tok/s

TTFT

19394 ms

Safe context

Memory

14.1 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 7B Instruct on RTX 2000 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: 10.0 tok/s decode · 19.4s TTFT (warm) · 25 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.1 GB, but this setup only exposes 8.0 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	19.8 tok/s	5339 ms	4K
Coding	F	Too heavy	10.0 tok/s	19394 ms	4K
Agentic Coding	F	Too heavy	6.6 tok/s	42895 ms	4K
Reasoning	F	Too heavy	10.0 tok/s	22921 ms	4K
RAG	F	Too heavy	6.6 tok/s	53619 ms	4K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A77
Q3_K_S	3	3.4 GB	Low	A77
NVFP4	4

Upgrade options

Hardware that runs CodeLlama 7B Instruct well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+8)448 GB/s (+192)

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

RTX 4060 Ti 16GBBest value

16 GB VRAM (+8)288 GB/s (+32)

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

RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)896 GB/s (+640)

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

Can RTX 2000 Ada Laptop 8GB run CodeLlama 7B Instruct?

No, CodeLlama 7B Instruct requires more memory than RTX 2000 Ada Laptop 8GB provides.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.1 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 7B Instruct run at on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, CodeLlama 7B Instruct achieves approximately 10.0 tokens per second decode speed with a time-to-first-token of 19394ms using Q4_K_M quantization.

Can RTX 2000 Ada Laptop 8GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RTX 2000 Ada Laptop 8GB receives a F grade with 10.0 tok/s and 4K context.

What context window can CodeLlama 7B Instruct use on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, CodeLlama 7B 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.

See all results for RTX 2000 Ada Laptop 8GB See all hardware for CodeLlama 7B Instruct

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