Can Gemma 4 26B A4B run on NVIDIA A100 40GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Gemma 4 26B A4B needs ~24.2 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~212 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 24.2 GB, 212.1 tok/s, Runs well

24.2 GB required40.0 GB available

61% VRAM used

Fit status

Runs well

Decode

212.1 tok/s

TTFT

913 ms

Safe context

85K

Memory

24.2 GB / 40.0 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B on NVIDIA A100 40GB
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: 212.1 tok/s decode · 913ms TTFT (warm) · 530 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	212.1 tok/s	498 ms	85K
Coding	S	Runs well	212.1 tok/s	913 ms	85K
Agentic Coding	S	Runs well	212.1 tok/s	1328 ms	85K
Reasoning	S	Runs well	212.1 tok/s	1079 ms	85K
RAG	S	Runs well	212.1 tok/s	1660 ms	85K

Inference speed

Gemma 4 26B A4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 26B A4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~195 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	195.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	124.4	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.2	Tight
RTX 3090 24GB	24 GB	Q4_K_M	106.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	90.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	75.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	71.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	55.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	55.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	39.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	38.7	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	35.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	34.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	13.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	8.5	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.8	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A79
Q3_K_S	3	12.3 GB	Low	A80
NVFP4	4	14.1 GB	Medium	A81
Q4_K_M	4	15.4 GB	Medium	A81
Q5_K_M	5	18.1 GB	High	A82
Q6_K	6	20.7 GB	High	A84
Q8_0Best for your GPU	8	27.0 GB	Very High	A83
F16	16	51.7 GB	Maximum	F0

Get started

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

Run

ollama run gemma4:26b

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s
Qwen 3.6 27B	27B	S	85.9 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	204.3 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Gemma 4 26B A4B?

Yes, NVIDIA A100 40GB can run Gemma 4 26B A4B with a S grade (Runs well). Expected decode speed: 212.1 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 24.2 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 NVIDIA A100 40GB?

On NVIDIA A100 40GB, Gemma 4 26B A4B achieves approximately 212.1 tokens per second decode speed with a time-to-first-token of 913ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on NVIDIA A100 40GB receives a S grade with 212.1 tok/s and 85K context.

What context window can Gemma 4 26B A4B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Gemma 4 26B A4B can safely use up to 85K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Gemma 4 26B A4B

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