Can CodeLlama 7B Instruct run on RTX 5080 Laptop 16GB?

YES — Tight Fit

A77Great

Estimated from fit model

CodeLlama 7B Instruct needs ~14.9 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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, 98.0 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

98.0 tok/s

TTFT

1976 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 5080 Laptop 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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	16K
Coding	A	Tight fit	98.0 tok/s	1976 ms	16K
Agentic Coding	F	Too heavy	54.3 tok/s	5187 ms	16K
Reasoning	A	Tight fit	98.0 tok/s	2335 ms	16K
RAG	F	Too heavy	54.3 tok/s	6484 ms	16K

Inference speed

CodeLlama 7B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for CodeLlama 7B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Tight
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	43.0	Heavy offload
RTX 3060 12GB	12 GB	Q4_K_M	24.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	10.6	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 5080 Laptop 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 5080 Laptop 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	126 tok/s
Qwen 3 14B	14B	S	81.6 tok/s
Qwen 3 8B	8B	S	112 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	77.3 tok/s
GPT-OSS 20B	21B	A	72.1 tok/s

Frequently asked questions

Can RTX 5080 Laptop 16GB run CodeLlama 7B Instruct?

Yes, RTX 5080 Laptop 16GB can run CodeLlama 7B Instruct with a A grade (Tight fit). Expected decode speed: 98.0 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 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, CodeLlama 7B Instruct achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 5080 Laptop 16GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RTX 5080 Laptop 16GB receives a A grade with 98.0 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 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 5080 Laptop 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 5080 Laptop 16GB See all hardware for CodeLlama 7B Instruct

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