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URL: https://willitrunai.com/can-run/qwen-2.5-coder-32b-on-a30-24gb

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Can Qwen 2.5 Coder 32B run on NVIDIA A30 24GB?

BARELY — Tight on Memory

B67Good
○Estimated from fit model

Qwen 2.5 Coder 32B needs ~26.7 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: 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) — 26.7 GB, 24.1 tok/s, Very compromised (needs ~2 GB host RAM)
26.7 GB required24.0 GB available
111% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2 GB host RAM)

Decode

24.1 tok/s

TTFT

8041 ms

Safe context

5K

Memory

26.7 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 32B on NVIDIA A30 24GB
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: 24.1 tok/s decode · 8.0s TTFT (warm) · 60 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload26.2 tok/s4037 ms5K
CodingBVery compromised22.3 tok/s8684 ms5K
Agentic CodingFToo heavy16.7 tok/s16832 ms5K
ReasoningBVery compromised22.3 tok/s10263 ms5K
RAGFToo heavy16.7 tok/s21040 ms5K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on NVIDIA A30 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowA78
Q3_K_S
3
15.7 GB
LowA77
NVFP4Best for your GPU

Get started

Copy-paste commands to run Qwen 2.5 Coder 32B on your machine.

Run

ollama run qwen2.5-coder

Upgrade options

Hardware that runs Qwen 2.5 Coder 32B well

👁 NVIDIA
RTX 5090 32GBBudget pick
32 GB VRAM (+8)1792 GB/s (+859)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.43 tok/s decode

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

Raises estimated decode speed by about 78%.

~$1,999 MSRP

👁 NVIDIA
RTX PRO 4500 Blackwell 32GBBest value
32 GB VRAM (+8)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.41.6 tok/s decode

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

Raises estimated decode speed by about 73%.

~$2,499 MSRP

👁 NVIDIA
RTX 5000 Ada 32GBNVIDIA upgrade
32 GB VRAM (+8)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.25.5 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

See all results for NVIDIA A30 24GBSee all hardware for Qwen 2.5 Coder 32B
4
17.9 GB
Medium
A77
Q4_K_M
4
19.5 GB
MediumF0
Q5_K_M
5
23.0 GB
HighF0
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 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.