Can Gemma 4 26B A4B run on RTX 4000 Ada 20GB?

Q: What should I upgrade first if Gemma 4 26B A4B feels slow on RTX 4000 Ada 20GB?

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

A75Great

Estimated from fit model

Gemma 4 26B A4B needs ~21.9 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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) — 21.9 GB, 28.2 tok/s, Very compromised (needs ~1.4 GB host RAM)

21.9 GB required20.0 GB available

110% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.4 GB host RAM)

Decode

28.2 tok/s

TTFT

6875 ms

Safe context

Memory

21.9 GB / 20.0 GB

Offload

10%

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B on RTX 4000 Ada 20GB
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: 28.2 tok/s decode · 6.9s TTFT (warm) · 70 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 1.4 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	Runs with offload (needs ~0.1 GB host RAM)	33.8 tok/s	3121 ms	8K
Coding	A	Very compromised (needs ~1.4 GB host RAM)	28.2 tok/s	6875 ms	8K
Agentic Coding	F	Too heavy	20.3 tok/s	13841 ms	8K
Reasoning	A	Very compromised	26.8 tok/s	8531 ms	8K
RAG	F	Too heavy	20.3 tok/s	17301 ms

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	S86
Q3_K_S	3	12.3 GB	Low	S85
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 26B A4B on your machine.

Run

ollama run gemma4:26b

Your hardware

More models your RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.8 tok/s
Qwen 3.5 27B	27B	A	10.7 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run Gemma 4 26B A4B?

Yes, RTX 4000 Ada 20GB can run Gemma 4 26B A4B with a A grade (Very compromised (needs ~1.4 GB host RAM)). Expected decode speed: 28.2 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 21.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 26B A4B?

The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 26B A4B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Gemma 4 26B A4B achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6875ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on RTX 4000 Ada 20GB receives a A grade with 28.2 tok/s and 8K context.

What context window can Gemma 4 26B A4B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Gemma 4 26B A4B can safely use up to 8K 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 26B A4B feels slow on RTX 4000 Ada 20GB?

See all results for RTX 4000 Ada 20GB See all hardware for Gemma 4 26B A4B

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