Can Granite Code 20B run on RTX 5080 Laptop 16GB?

Q: What should I upgrade first if Granite Code 20B feels slow on RTX 5080 Laptop 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

A70Great

Estimated from fit model

Granite Code 20B needs ~17.9 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 17.9 GB, 33.9 tok/s, Very compromised (needs ~1.3 GB host RAM)

17.9 GB required16.0 GB available

112% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.3 GB host RAM)

Decode

33.9 tok/s

TTFT

5707 ms

Safe context

Memory

17.9 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite Code 20B 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: 33.9 tok/s decode · 5.7s TTFT (warm) · 85 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.2 GB host RAM)	41.3 tok/s	2559 ms	7K
Coding	A	Very compromised (needs ~1.3 GB host RAM)	33.9 tok/s	5707 ms	7K
Agentic Coding	F	Too heavy	24.0 tok/s	11709 ms	7K
Reasoning	A	Very compromised (needs ~1.3 GB host RAM)	33.9 tok/s	6744 ms	7K
RAG	F	Too heavy	24.0 tok/s	14637 ms

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on RTX 5080 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	A81
Q3_K_S	3	9.8 GB	Low	A81
NVFP4	4

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Your hardware

More models your RTX 5080 Laptop 16GB can run

Model	Params	Grade	Decode	Capabilities
GPT-OSS 20B	21B	A	74.6 tok/s
Codestral 2 25.08	22B	A

Frequently asked questions

Can RTX 5080 Laptop 16GB run Granite Code 20B?

Yes, RTX 5080 Laptop 16GB can run Granite Code 20B with a A grade (Very compromised (needs ~1.3 GB host RAM)). Expected decode speed: 33.9 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 17.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, Granite Code 20B achieves approximately 33.9 tokens per second decode speed with a time-to-first-token of 5707ms using Q4_K_M quantization.

Can RTX 5080 Laptop 16GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on RTX 5080 Laptop 16GB receives a A grade with 33.9 tok/s and 7K context.

What context window can Granite Code 20B use on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, Granite Code 20B can safely use up to 7K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on RTX 5080 Laptop 16GB?

See all results for RTX 5080 Laptop 16GB See all hardware for Granite Code 20B

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