Can Qwen 2.5 32B run on NVIDIA L4 24GB?

BARELY — Tight on Memory

B69Good

Estimated from fit model

Qwen 2.5 32B needs ~27.0 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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) — 27.0 GB, 6.3 tok/s, Very compromised (needs ~2.2 GB host RAM)

27.0 GB required24.0 GB available

113% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.2 GB host RAM)

Decode

6.3 tok/s

TTFT

30721 ms

Safe context

Memory

27.0 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B on NVIDIA L4 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: 6.3 tok/s decode · 30.7s TTFT (warm) · 16 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 2.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.8 GB host RAM)	7.4 tok/s	14309 ms	4K
Coding	B	Very compromised (needs ~2.2 GB host RAM)	6.3 tok/s	30721 ms	4K
Agentic Coding	F	Too heavy	4.7 tok/s	59371 ms	4K
Reasoning	B	Very compromised (needs ~2.2 GB host RAM)	6.3 tok/s	36306 ms	4K
RAG	F	Too heavy	4.7 tok/s	74213 ms	4K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A84
Q3_K_S	3	15.7 GB	Low	A83
NVFP4Best for your GPU	4	17.9 GB	Medium	A83
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

Get started

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

Run

ollama run qwen2.5

Upgrade-Optionen

Hardware, die Qwen 2.5 32B gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1492)

Removes host-memory offload, which is usually the single biggest latency and throughput win.66.4 tok/s Dekodierung

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

Raises estimated decode speed by about 954%.

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)896 GB/s (+596)

Removes host-memory offload, which is usually the single biggest latency and throughput win.41.6 tok/s Dekodierung

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

Raises estimated decode speed by about 560%.

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

32 GB VRAM (+8)576 GB/s (+276)

Removes host-memory offload, which is usually the single biggest latency and throughput win.25.5 tok/s Dekodierung

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

Raises estimated decode speed by about 305%.

ca. $4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Qwen 2.5 32B?

Yes, NVIDIA L4 24GB can run Qwen 2.5 32B with a B grade (Very compromised (needs ~2.2 GB host RAM)). Expected decode speed: 6.3 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 27.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization for Qwen 2.5 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 32B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Qwen 2.5 32B achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30721ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on NVIDIA L4 24GB receives a B grade with 6.3 tok/s and 4K context.

What context window can Qwen 2.5 32B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Qwen 2.5 32B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 32B feels slow on NVIDIA L4 24GB?

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.

See all results for NVIDIA L4 24GB See all hardware for Qwen 2.5 32B

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