Can Gemma 3 27B run on NVIDIA V100 32GB?

YES — With Offload

A83Great

Estimated from fit model

Gemma 3 27B needs ~32.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 32.1 GB, 32.6 tok/s, Runs with offload (needs ~0.1 GB host RAM)

32.1 GB required32.0 GB available

100% VRAM needed

0.1 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.1 GB host RAM)

Decode

32.6 tok/s

TTFT

5935 ms

Safe context

16K

Memory

32.1 GB / 32.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 3 27B on NVIDIA V100 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: 32.6 tok/s decode · 5.9s TTFT (warm) · 82 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	38.4 tok/s	2747 ms	16K
Coding	A	Runs with offload (needs ~0.1 GB host RAM)	32.6 tok/s	5935 ms	16K
Agentic Coding	F	Too heavy	19.9 tok/s	14162 ms	16K
Reasoning	A	Runs with offload (needs ~0.1 GB host RAM)	32.6 tok/s	7014 ms	16K
RAG	F	Too heavy	19.9 tok/s	17702 ms	16K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A79
Q3_K_S	3	13.2 GB	Low	A80
NVFP4	4	15.1 GB	Medium	A81
Q4_K_M	4	16.5 GB	Medium	A82
Q5_K_M	5	19.4 GB	High	A82
Q6_KBest for your GPU	6	22.1 GB	High	A81
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

Your hardware

More models your NVIDIA V100 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s
Qwen 3.5 35B A3B	35B	S	83.3 tok/s
Qwen 3 32B	32B	S	33.6 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Gemma 3 27B?

Yes, NVIDIA V100 32GB can run Gemma 3 27B with a A grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 32.6 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 32.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Gemma 3 27B achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5935ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA V100 32GB receives a A grade with 32.6 tok/s and 16K context.

What context window can Gemma 3 27B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Gemma 3 27B can safely use up to 16K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 27B feels slow on NVIDIA V100 32GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA V100 32GB See all hardware for Gemma 3 27B

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