Can Qwen 2.5 Coder 0.5B run on NVIDIA L40 48GB?

YES — Runs Great

C46Usable

Estimated from fit model

Qwen 2.5 Coder 0.5B needs ~6.2 GB VRAM. NVIDIA L40 48GB has 48.0 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) — 6.2 GB, 8.0 tok/s, Runs well

6.2 GB required48.0 GB available

13% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24200 ms

Safe context

131K

Memory

6.2 GB / 48.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 0.5B on NVIDIA L40 48GB
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 NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	C50
Q3_K_S	3	0.2 GB	Low	C50
NVFP4	4	0.3 GB	Medium	C50
Q4_K_M	4	0.3 GB	Medium	C50
Q5_K_M	5	0.4 GB	High	C50
Q6_K	6	0.4 GB	High	C50
Q8_0	8	0.5 GB	Very High	C50
F16Best for your GPU	16	1.0 GB	Maximum	C50

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 M4 Max 96GBBudget pick

96 GB Unified (+48)

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.

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+80)

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.

~$2,499 MSRP

Frequently asked questions

Can NVIDIA L40 48GB run Qwen 2.5 Coder 0.5B?

Yes, NVIDIA L40 48GB 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 6.2 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 NVIDIA L40 48GB?

On NVIDIA L40 48GB, 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 NVIDIA L40 48GB run Qwen 2.5 Coder 0.5B for coding?

For coding workloads, Qwen 2.5 Coder 0.5B on NVIDIA L40 48GB receives a C grade with 8.0 tok/s and 131K context.

What context window can Qwen 2.5 Coder 0.5B use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, 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 NVIDIA L40 48GB 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-l40-48gb" 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

8.0 tok/s

13200 ms

131K

Coding

Runs well

8.0 tok/s

24200 ms

131K

Agentic Coding

Runs well

8.0 tok/s

35200 ms

131K

Reasoning

Runs well

8.0 tok/s

28600 ms

131K

RAG

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 NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	C50
Q3_K_S	3	0.2 GB	Low	C50
NVFP4	4	0.3 GB	Medium	C50
Q4_K_M	4	0.3 GB	Medium	C50
Q5_K_M	5	0.4 GB	High	C50
Q6_K	6	0.4 GB	High	C50
Q8_0	8	0.5 GB	Very High	C50
F16Best for your GPU	16	1.0 GB	Maximum	C50

Frequently asked questions

Can NVIDIA L40 48GB run Qwen 2.5 Coder 0.5B?

Yes, NVIDIA L40 48GB 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 6.2 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 NVIDIA L40 48GB?

On NVIDIA L40 48GB, 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 NVIDIA L40 48GB run Qwen 2.5 Coder 0.5B for coding?

For coding workloads, Qwen 2.5 Coder 0.5B on NVIDIA L40 48GB receives a C grade with 8.0 tok/s and 131K context.

What context window can Qwen 2.5 Coder 0.5B use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, 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.