Can Gemma 4 31B run on NVIDIA L20 48GB?

YES — Runs Great

S88Excellent

○Estimated from fit model

Gemma 4 31B needs ~39.1 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~14 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) — 39.1 GB, 13.9 tok/s, Runs well

39.1 GB required48.0 GB available

81% VRAM used

Fit status

Runs well

Decode

13.9 tok/s

TTFT

13893 ms

Safe context

26K

Memory

39.1 GB / 48.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGemma 4 31B on NVIDIA L20 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: 13.9 tok/s decode · 13.9s TTFT (warm) · 35 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	S	Runs well	13.9 tok/s	7578 ms	26K
Coding	S	Runs well	13.9 tok/s	13893 ms	26K
Agentic Coding	A	Very compromised (needs ~2 GB host RAM)	8.2 tok/s	34155 ms	26K
Reasoning	S	Runs well	13.9 tok/s	16419 ms	26K
RAG	A	Very compromised (needs ~2 GB host RAM)	8.2 tok/s	42694 ms	26K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A80
Q3_K_S	3	15.0 GB	Low	A81
NVFP4	4	17.2 GB	Medium	A82
Q4_K_M	4	18.7 GB	Medium	A83
Q5_K_M	5	22.1 GB	High	A84
Q6_K	6	25.2 GB	High	A85
Q8_0Best for your GPU	8	32.8 GB	Very High	A85
F16	16	62.9 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 31B on your machine.

Run

ollama run gemma4:31b

Your hardware

More models your NVIDIA L20 48GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.6 35B A3B	35B	S	85.8 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	93.3 tok/s
👁 Alibaba Qwen 3 32B	32B	S	18 tok/s
👁 Alibaba Qwen 2.5 VL 72B	72B	A	5.2 tok/s
👁 Alibaba Qwen3-Coder-Next	80B	A	23.1 tok/s

Frequently asked questions

See all results for NVIDIA L20 48GB See all hardware for Gemma 4 31B

URL: https://willitrunai.com/can-run/gemma-4-31b-on-l20-48gb