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	8K

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	11.2 GB	Medium	C51
Q4_K_M	4	12.2 GB	Medium	C51
Q5_K_M	5	14.4 GB	High	C52
Q6_K	6	16.4 GB	High	C53
Q8_0	8	21.4 GB	Very High	C54
F16Best for your GPU	16	41.0 GB	Maximum	B56

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