Can Gemma 4 31B run on RTX 5000 Ada Laptop 16GB?

Q: What should I upgrade first if Gemma 4 31B feels slow on RTX 5000 Ada Laptop 16GB?

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.

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 4 31B needs ~36.2 GB but RTX 5000 Ada Laptop 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: 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

Q4_K_M (Medium quality) — 36.2 GB, exceeds 16.0 GB available

36.2 GB required16.0 GB available

226% VRAM needed

20.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.5 tok/s

TTFT

54745 ms

Safe context

Memory

36.2 GB / 16.0 GB

Offload

60%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 31B on RTX 5000 Ada Laptop 16GB
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: 3.5 tok/s decode · 54.7s 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 36.2 GB, but this setup only exposes 16.0 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	F	Too heavy	5.1 tok/s	20659 ms	4K
Coding	F	Too heavy	3.5 tok/s	54745 ms	4K
Agentic Coding	F	Too heavy	3.5 tok/s	79630 ms	4K
Reasoning	F	Too heavy	3.5 tok/s	64699 ms	4K
RAG	F	Too heavy	3.5 tok/s	99537 ms	4K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	F0
Q3_K_S	3	15.0 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Gemma 4 31B well

RTX 5090 32GBBest value

32 GB VRAM (+16)1792 GB/s (+1216)

Makes the model fit on the accelerator instead of staying completely out of reach.36.7 tok/s decode

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

Raises estimated decode speed by about 949%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBNVIDIA upgrade

32 GB VRAM (+16)896 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.23 tok/s decode

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

Raises estimated decode speed by about 557%.

~$2,499 MSRP

RTX A6000 48GBBudget pick

48 GB VRAM (+32)768 GB/s (+192)

Makes the model fit on the accelerator instead of staying completely out of reach.32.7 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 RTX 5000 Ada Laptop 16GB run Gemma 4 31B?

No, Gemma 4 31B requires more memory than RTX 5000 Ada Laptop 16GB provides.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 36.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Gemma 4 31B achieves approximately 3.5 tokens per second decode speed with a time-to-first-token of 54745ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RTX 5000 Ada Laptop 16GB receives a F grade with 3.5 tok/s and 4K context.

What context window can Gemma 4 31B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Gemma 4 31B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 31B feels slow on RTX 5000 Ada Laptop 16GB?

See all results for RTX 5000 Ada Laptop 16GB See all hardware for Gemma 4 31B

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