Can Qwen 2.5 Coder 14B run on RTX 2080 Ti 11GB?

YES — With NVFP4

C48Usable

Estimated from fit model

Qwen 2.5 Coder 14B needs ~13.1 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With NVFP4 quantization, expect ~29 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

Qwen 2.5 Coder 14B at Q4_K_M needs 13.8 GB — too much for RTX 2080 Ti 11GB (11.0 GB). Runs at NVFP4 (13.1 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.8 GB, exceeds 11.0 GB available

13.8 GB required11.0 GB available

125% VRAM needed

2.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

22.6 tok/s

TTFT

8583 ms

Safe context

Memory

13.8 GB / 11.0 GB

Offload

20%

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 Coder 14B on RTX 2080 Ti 11GB
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: 22.6 tok/s decode · 8.6s TTFT (warm) · 56 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 20% 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 1.2 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	C	Very compromised (needs ~0.9 GB host RAM)	28.9 tok/s	3657 ms	4K
Coding	F	Too heavy	22.6 tok/s	8583 ms	4K
Agentic Coding	F	Too heavy	14.8 tok/s	19070 ms	4K
Reasoning	F	Too heavy	22.6 tok/s	10144 ms	4K
RAG	F	Too heavy	14.8 tok/s	23838 ms	4K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B67
Q3_K_S	3	6.9 GB	Low	B66
NVFP4Best for your GPU	4	7.8 GB	Medium	B66
Q4_K_M	4	8.5 GB	Medium	F0
Q5_K_M	5	10.1 GB	High	F0
Q6_K	6	11.5 GB	High	F0
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

Opciones de mejora

Hardware que ejecuta bien Qwen 2.5 Coder 14B

RTX 3060 12GBMejor relación calidad-precio

12 GB VRAM (+1)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.16.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

~$329 MSRP

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+5)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.35.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+5)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.26.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$499 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run Qwen 2.5 Coder 14B?

Yes, RTX 2080 Ti 11GB can run Qwen 2.5 Coder 14B at NVFP4 quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q4_K_M requires 13.8 GB which exceeds available memory, but at NVFP4 it needs only 13.1 GB. Expected decode speed: 28.9 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 13.8 GB at Q4_K_M quantization. On RTX 2080 Ti 11GB, it fits at NVFP4 using 13.1 GB.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization is Q4_K_M, but on RTX 2080 Ti 11GB the best fitting quantization is NVFP4, which uses 13.1 GB.

What speed will Qwen 2.5 Coder 14B run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Qwen 2.5 Coder 14B achieves approximately 28.9 tokens per second decode speed with a time-to-first-token of 6692ms using NVFP4 quantization.

Can RTX 2080 Ti 11GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on RTX 2080 Ti 11GB receives a F grade with 22.6 tok/s and 4K context.

What context window can Qwen 2.5 Coder 14B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Qwen 2.5 Coder 14B can safely use up to 5K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 Coder 14B feels slow on RTX 2080 Ti 11GB?

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.

See all results for RTX 2080 Ti 11GB See all hardware for Qwen 2.5 Coder 14B

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