Can CodeLlama 7B Instruct run on RTX 2000 Ada 16GB?

YES — Tight Fit

A75Great

Estimated from fit model

CodeLlama 7B Instruct needs ~14.9 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

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) — 14.9 GB, 51.3 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

51.3 tok/s

TTFT

3777 ms

Safe context

16K

Memory

14.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on RTX 2000 Ada 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: 51.3 tok/s decode · 3.8s TTFT (warm) · 128 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	51.3 tok/s	2060 ms	16K
Coding	A	Tight fit	51.3 tok/s	3777 ms	16K
Agentic Coding	F	Too heavy	18.4 tok/s	15288 ms	16K
Reasoning	A	Tight fit	51.3 tok/s	4464 ms	16K
RAG	F	Too heavy	18.4 tok/s	19110 ms	16K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4	3.9 GB	Medium	A71
Q4_K_M	4	4.3 GB	Medium	A71
Q5_K_M	5	5.0 GB	High	A72
Q6_K	6	5.7 GB	High	A73
Q8_0Best for your GPU	8	7.5 GB	Very High	A75
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Your hardware

More models your RTX 2000 Ada 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	42.9 tok/s
Qwen 3 14B	14B	S	27.7 tok/s
Qwen 3 8B	8B	S	48.2 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	26.2 tok/s
GPT-OSS 20B	21B	A	24.4 tok/s

Frequently asked questions

Can RTX 2000 Ada 16GB run CodeLlama 7B Instruct?

Yes, RTX 2000 Ada 16GB can run CodeLlama 7B Instruct with a A grade (Tight fit). Expected decode speed: 51.3 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.9 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 16GB?

On RTX 2000 Ada 16GB, CodeLlama 7B Instruct achieves approximately 51.3 tokens per second decode speed with a time-to-first-token of 3777ms using Q4_K_M quantization.

Can RTX 2000 Ada 16GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RTX 2000 Ada 16GB receives a A grade with 51.3 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on RTX 2000 Ada 16GB?

On RTX 2000 Ada 16GB, CodeLlama 7B Instruct can safely use up to 16K 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 7B Instruct feels slow on RTX 2000 Ada 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 2000 Ada 16GB See all hardware for CodeLlama 7B Instruct

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