Can Qwen 2.5 Coder 0.5B run on RTX 4090 24GB?

YES — Runs Great

C46Usable

Estimated from fit model

Qwen 2.5 Coder 0.5B needs ~3.8 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 3.8 GB, 8.0 tok/s, Runs well

3.8 GB required24.0 GB available

16% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24200 ms

Safe context

131K

Memory

3.8 GB / 24.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 0.5B on RTX 4090 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: 8.0 tok/s decode · 24.2s TTFT (warm) · 20 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	8.0 tok/s	13200 ms	131K
Coding	C	Runs well	8.0 tok/s	24200 ms	131K
Agentic Coding	C	Runs well	8.0 tok/s	35200 ms	131K
Reasoning	C	Runs well	8.0 tok/s	28600 ms	131K
RAG	C	Runs well	8.0 tok/s	44000 ms	131K

Quantization options

How Qwen 2.5 Coder 0.5B (0.5B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	C52
Q3_K_S	3	0.2 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 0.5B on your machine.

Run

ollama run qwen2.5-coder:0.5b

Upgrade options

Hardware that runs Qwen 2.5 Coder 0.5B well

Mac mini M4 64GBBest value

64 GB Unified (+40)

Adds memory headroom for longer context windows and future model growth.7 tok/s decode

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

~$1,099 MSRP

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+784)

Adds memory headroom for longer context windows and future model growth.9.5 tok/s decode

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

~$1,999 MSRP

Frequently asked questions

Can RTX 4090 24GB run Qwen 2.5 Coder 0.5B?

Yes, RTX 4090 24GB can run Qwen 2.5 Coder 0.5B with a C grade (Runs well). Expected decode speed: 8.0 tok/s.

How much VRAM does Qwen 2.5 Coder 0.5B need?

Qwen 2.5 Coder 0.5B (0.5B parameters) requires approximately 3.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 0.5B?

The recommended quantization for Qwen 2.5 Coder 0.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 0.5B run at on RTX 4090 24GB?

On RTX 4090 24GB, Qwen 2.5 Coder 0.5B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24200ms using Q4_K_M quantization.

Can RTX 4090 24GB run Qwen 2.5 Coder 0.5B for coding?

For coding workloads, Qwen 2.5 Coder 0.5B on RTX 4090 24GB receives a C grade with 8.0 tok/s and 131K context.

What context window can Qwen 2.5 Coder 0.5B use on RTX 4090 24GB?

On RTX 4090 24GB, Qwen 2.5 Coder 0.5B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4090 24GB See all hardware for Qwen 2.5 Coder 0.5B

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