Can Qwen 2.5 Coder 0.5B run on RTX 5090 32GB?

YES — Runs Great

C47Usable

Estimated from fit model

Qwen 2.5 Coder 0.5B needs ~4.6 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~10 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) — 4.6 GB, 9.5 tok/s, Runs well

4.6 GB required32.0 GB available

14% VRAM used

Fit status

Runs well

Decode

9.5 tok/s

TTFT

20379 ms

Safe context

131K

Memory

4.6 GB / 32.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 0.5B on RTX 5090 32GB
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: 9.5 tok/s decode · 20.4s TTFT (warm) · 24 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	9.5 tok/s	11116 ms	131K
Coding	C	Runs well	9.5 tok/s	20379 ms	131K
Agentic Coding	C	Runs well	9.5 tok/s	29642 ms	131K
Reasoning	C	Runs well	9.5 tok/s	24084 ms	131K
RAG	C	Runs well	9.5 tok/s	37053 ms	131K

Inference speed

Qwen 2.5 Coder 0.5B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 0.5B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~10 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	9.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	8.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	7.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	7.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	7.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	7.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	7.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	7.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	7.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	7.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	7.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	7.0	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 2.5 Coder 0.5B (0.5B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	C51
Q3_K_S	3	0.2 GB	Low	C51
NVFP4	4	0.3 GB	Medium	C51
Q4_K_M	4	0.3 GB	Medium	C51
Q5_K_M	5	0.4 GB	High	C51
Q6_K	6	0.4 GB	High	C51
Q8_0	8	0.5 GB	Very High	C51
F16Best for your GPU	16	1.0 GB	Maximum	C52

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

MacBook Pro M3 Max 48GBBudget pick

48 GB Unified (+16)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.7 tok/s decode

~$2,499 MSRP

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+16)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.7 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can RTX 5090 32GB run Qwen 2.5 Coder 0.5B?

Yes, RTX 5090 32GB can run Qwen 2.5 Coder 0.5B with a C grade (Runs well). Expected decode speed: 9.5 tok/s.

How much VRAM does Qwen 2.5 Coder 0.5B need?

Qwen 2.5 Coder 0.5B (0.5B parameters) requires approximately 4.6 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 5090 32GB?

On RTX 5090 32GB, Qwen 2.5 Coder 0.5B achieves approximately 9.5 tokens per second decode speed with a time-to-first-token of 20379ms using Q4_K_M quantization.

Can RTX 5090 32GB run Qwen 2.5 Coder 0.5B for coding?

For coding workloads, Qwen 2.5 Coder 0.5B on RTX 5090 32GB receives a C grade with 9.5 tok/s and 131K context.

What context window can Qwen 2.5 Coder 0.5B use on RTX 5090 32GB?

On RTX 5090 32GB, 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 5090 32GB See all hardware for Qwen 2.5 Coder 0.5B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-0.5b-on-rtx-5090-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

9.5 tok/s

11116 ms

131K

Coding

Runs well

9.5 tok/s

20379 ms

131K

Agentic Coding

Runs well

9.5 tok/s

29642 ms

131K

Reasoning

Runs well

9.5 tok/s

24084 ms

131K

RAG

Runs well

9.5 tok/s

37053 ms

131K

Qwen 2.5 Coder 0.5B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

9.5

Fits

RTX 4090 24GB

24 GB

Q4_K_M

8.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

7.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

7.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

7.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

7.0

Fits

RTX 4060 8GB

8 GB

Q4_K_M

7.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

7.0

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

7.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

7.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

7.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

7.0

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

7.0

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

7.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

7.0

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

7.0

Fits

Quantization options

How Qwen 2.5 Coder 0.5B (0.5B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	C51
Q3_K_S	3	0.2 GB	Low	C51
NVFP4	4	0.3 GB	Medium	C51
Q4_K_M	4	0.3 GB	Medium	C51
Q5_K_M	5	0.4 GB	High	C51
Q6_K	6	0.4 GB	High	C51
Q8_0	8	0.5 GB	Very High	C51
F16Best for your GPU	16	1.0 GB	Maximum	C52

Frequently asked questions

Can RTX 5090 32GB run Qwen 2.5 Coder 0.5B?

Yes, RTX 5090 32GB can run Qwen 2.5 Coder 0.5B with a C grade (Runs well). Expected decode speed: 9.5 tok/s.

How much VRAM does Qwen 2.5 Coder 0.5B need?

Qwen 2.5 Coder 0.5B (0.5B parameters) requires approximately 4.6 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 5090 32GB?

On RTX 5090 32GB, Qwen 2.5 Coder 0.5B achieves approximately 9.5 tokens per second decode speed with a time-to-first-token of 20379ms using Q4_K_M quantization.

Can RTX 5090 32GB run Qwen 2.5 Coder 0.5B for coding?

For coding workloads, Qwen 2.5 Coder 0.5B on RTX 5090 32GB receives a C grade with 9.5 tok/s and 131K context.

What context window can Qwen 2.5 Coder 0.5B use on RTX 5090 32GB?

On RTX 5090 32GB, 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.