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URL: https://willitrunai.com/can-run/qwen-2.5-coder-14b-on-a2000-12gb

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Can Qwen 2.5 Coder 14B run on RTX A2000 12GB?

BARELY — Tight on Memory

C53Usable
○Estimated from fit model

Qwen 2.5 Coder 14B needs ~13.6 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) — 13.6 GB, 16.4 tok/s, Very compromised (needs ~1 GB host RAM)
13.6 GB required12.0 GB available
113% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

16.4 tok/s

TTFT

11770 ms

Safe context

7K

Memory

13.6 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on RTX A2000 12GB
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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload (needs ~0.1 GB host RAM)20.9 tok/s5048 ms7K
CodingCVery compromised (needs ~1 GB host RAM)16.4 tok/s11770 ms7K
Agentic CodingFToo heavy10.9 tok/s25835 ms7K
ReasoningCVery compromised (needs ~1 GB host RAM)16.4 tok/s13910 ms7K
RAGFToo heavy10.9 tok/s32294 ms

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowB67
Q3_K_S
3
6.9 GB
LowB66
NVFP4
4

Get started

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

Run

ollama run qwen2.5-coder:14b

Upgrade options

Hardware that runs Qwen 2.5 Coder 14B well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+4)448 GB/s (+160)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.34.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 108%.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value
16 GB VRAM (+4)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.27.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 70%.

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade
16 GB VRAM (+4)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.27.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 69%.

~$625 MSRP

Frequently asked questions

See all results for RTX A2000 12GBSee all hardware for Qwen 2.5 Coder 14B
7K
7.8 GB
Medium
B66
Q4_K_MBest for your GPU
4
8.5 GB
MediumB66
Q5_K_M
5
10.1 GB
HighF0
Q6_K
6
11.5 GB
HighF0
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

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.