Can InternLM 20B run on NVIDIA L4 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

InternLM 20B needs ~36.0 GB but NVIDIA L4 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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

Q5_K_M (High quality) — 36.0 GB, exceeds 24.0 GB available

36.0 GB required24.0 GB available

150% VRAM needed

12.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.6 tok/s

TTFT

54187 ms

Safe context

Memory

36.0 GB / 24.0 GB

Offload

30%

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsInternLM 20B 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: 3.6 tok/s decode · 54.2s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 36.0 GB, but this setup only exposes 24.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Very compromised (needs ~1.5 GB host RAM)	6.6 tok/s	15940 ms	6K
Coding	F	Too heavy	3.6 tok/s	54187 ms	6K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	6K
Reasoning	F	Too heavy	3.6 tok/s	64040 ms	6K
RAG	F	Too heavy	2.0 tok/s	176000 ms	6K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	B55
Q3_K_S	3	9.8 GB	Low	B57
NVFP4	4

Upgrade options

Hardware that runs InternLM 20B well

RTX 5090 32GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.44.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 1136%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.30.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 750%.

~$2,499 MSRP

RTX A6000 48GBBudget pick

48 GB VRAM (+24)768 GB/s (+468)

Makes the model fit on the accelerator instead of staying completely out of reach.41.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$4,650 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run InternLM 20B?

No, InternLM 20B requires more memory than NVIDIA L4 24GB provides.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 36.0 GB of memory with Q5_K_M quantization.

What is the best quantization for InternLM 20B?

The recommended quantization for InternLM 20B is Q5_K_M, which balances quality and memory efficiency.

What speed will InternLM 20B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, InternLM 20B achieves approximately 3.6 tokens per second decode speed with a time-to-first-token of 54187ms using Q5_K_M quantization.

Can NVIDIA L4 24GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on NVIDIA L4 24GB receives a F grade with 3.6 tok/s and 6K context.

What context window can InternLM 20B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, InternLM 20B can safely use up to 6K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if InternLM 20B feels slow on NVIDIA L4 24GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for NVIDIA L4 24GB See all hardware for InternLM 20B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/internlm-20b-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: