Can Gemma 4 31B run on RTX 5000 Ada 32GB?

Q: What should I upgrade first if Gemma 4 31B feels slow on RTX 5000 Ada 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

A74Great

Estimated from fit model

Gemma 4 31B needs ~37.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 37.5 GB, 10.5 tok/s, Very compromised (needs ~2.7 GB host RAM)

37.5 GB required32.0 GB available

117% VRAM needed

5.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.7 GB host RAM)

Decode

10.5 tok/s

TTFT

18383 ms

Safe context

10K

Memory

37.5 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 4 31B on RTX 5000 Ada 32GB
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: 10.5 tok/s decode · 18.4s TTFT (warm) · 26 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	19.6 tok/s	5393 ms	10K
Coding	A	Very compromised (needs ~2.7 GB host RAM)	10.5 tok/s	18383 ms	10K
Agentic Coding	F	Too heavy	5.3 tok/s	53551 ms	10K
Reasoning	A	Very compromised (needs ~2.7 GB host RAM)	10.5 tok/s	21725 ms	10K
RAG	F	Too heavy	5.3 tok/s	66939 ms

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A84
Q3_K_S	3	15.0 GB	Low	S86
NVFP4	4

Get started

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

Run

ollama run gemma4:31b

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	58.6 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Gemma 4 31B?

Yes, RTX 5000 Ada 32GB can run Gemma 4 31B with a A grade (Very compromised (needs ~2.7 GB host RAM)). Expected decode speed: 10.5 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 37.5 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 32GB?

On RTX 5000 Ada 32GB, Gemma 4 31B achieves approximately 10.5 tokens per second decode speed with a time-to-first-token of 18383ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RTX 5000 Ada 32GB receives a A grade with 10.5 tok/s and 10K context.

What context window can Gemma 4 31B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Gemma 4 31B can safely use up to 10K 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 32GB?

See all results for RTX 5000 Ada 32GB See all hardware for Gemma 4 31B

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