Can InternLM 20B run on NVIDIA L40 48GB?

YES — Runs Great

B62Good

Estimated from fit model

InternLM 20B needs ~38.4 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q5_K_M quantization, expect ~39 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

Q5_K_M (High quality) — 38.4 GB, 38.7 tok/s, Runs well

38.4 GB required48.0 GB available

80% VRAM used

Fit status

Runs well

Decode

38.7 tok/s

TTFT

5005 ms

Safe context

Memory

38.4 GB / 48.0 GB

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsInternLM 20B 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: 38.7 tok/s decode · 5.0s TTFT (warm) · 97 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	B	Runs well	38.7 tok/s	2730 ms	8K
Coding	B	Runs well	38.7 tok/s	5005 ms	8K
Agentic Coding	C	Very compromised (needs ~2.2 GB host RAM)	20.4 tok/s	13795 ms	8K
Reasoning	B	Runs well	38.7 tok/s	5916 ms	8K
RAG	C	Very compromised (needs ~2.2 GB host RAM)	20.4 tok/s	17244 ms

Quantization options

How InternLM 20B (20B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C50
Q3_K_S	3	9.8 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run InternLM 20B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "internlm/internlm2_5-20b-chat" \
  --hf-file "internlm2_5-20b-chat-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs InternLM 20B well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+774)

Raises estimated decode speed by about 104%.78.9 tok/s decode

Raises estimated decode speed by about 104%.

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

~$10,000 MSRP

Frequently asked questions

Can NVIDIA L40 48GB run InternLM 20B?

Yes, NVIDIA L40 48GB can run InternLM 20B with a B grade (Runs well). Expected decode speed: 38.7 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 38.4 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 L40 48GB?

On NVIDIA L40 48GB, InternLM 20B achieves approximately 38.7 tokens per second decode speed with a time-to-first-token of 5005ms using Q5_K_M quantization.

Can NVIDIA L40 48GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on NVIDIA L40 48GB receives a B grade with 38.7 tok/s and 8K context.

What context window can InternLM 20B use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, InternLM 20B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for NVIDIA L40 48GB See all hardware for InternLM 20B

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