Can Gemma 3 27B run on NVIDIA A100 80GB?

YES — Runs Great

A84Great

Estimated from fit model

Gemma 3 27B needs ~36.9 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~109 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) — 36.9 GB, 109.2 tok/s, Runs well

36.9 GB required80.0 GB available

46% VRAM used

Fit status

Runs well

Decode

109.2 tok/s

TTFT

1773 ms

Safe context

77K

Memory

36.9 GB / 80.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsGemma 3 27B on NVIDIA A100 80GB
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: 109.2 tok/s decode · 1.8s TTFT (warm) · 273 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	A	Runs well	109.2 tok/s	967 ms	77K
Coding	A	Runs well	109.2 tok/s	1773 ms	77K
Agentic Coding	S	Runs well	109.2 tok/s	2579 ms	77K
Reasoning	A	Runs well	109.2 tok/s	2095 ms	77K
RAG	S	Runs well	109.2 tok/s	3224 ms	77K

Inference speed

Gemma 3 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 3 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~58 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	58.2	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	26.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	26.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	26.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	22.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.3	Fits
RTX 4090 24GB	24 GB	Q4_K_M	20.9	Too big
RTX 3090 24GB	24 GB	Q4_K_M	17.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	16.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	12.6	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	11.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	10.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.5	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 3 27B (27B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A73
Q3_K_S	3	13.2 GB	Low	A73
NVFP4	4	15.1 GB	Medium	A73
Q4_K_M	4	16.5 GB	Medium	A74
Q5_K_M	5	19.4 GB	High	A74
Q6_K	6	22.1 GB	High	A75
Q8_0	8	28.9 GB	Very High	A76
F16Best for your GPU	16	55.4 GB	Maximum	A80

Get started

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

Run

ollama run gemma3

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	259 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s
Qwen 3.6 35B A3B	35B	S	217.7 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	267.8 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Gemma 3 27B?

Yes, NVIDIA A100 80GB can run Gemma 3 27B with a A grade (Runs well). Expected decode speed: 109.2 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 36.9 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 A100 80GB?

On NVIDIA A100 80GB, Gemma 3 27B achieves approximately 109.2 tokens per second decode speed with a time-to-first-token of 1773ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA A100 80GB receives a A grade with 109.2 tok/s and 77K context.

What context window can Gemma 3 27B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Gemma 3 27B can safely use up to 77K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Gemma 3 27B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

109.2 tok/s

967 ms

77K

Coding

Runs well

109.2 tok/s

1773 ms

77K

Agentic Coding

Runs well

109.2 tok/s

2579 ms

77K

Reasoning

Runs well

109.2 tok/s

2095 ms

77K

RAG

Runs well

109.2 tok/s

3224 ms

77K

Gemma 3 27B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

58.2

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

26.9

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

26.6

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

26.6

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

22.4

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

21.3

Fits

RTX 4090 24GB

24 GB

Q4_K_M

20.9

Too big

RTX 3090 24GB

24 GB

Q4_K_M

17.9

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

16.8

Offloads

RX 7900 XTX 24GB

24 GB

Q4_K_M

12.6

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

11.6

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

10.6

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

7.5

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.6

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.3

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A73
Q3_K_S	3	13.2 GB	Low	A73
NVFP4	4	15.1 GB	Medium	A73
Q4_K_M	4	16.5 GB	Medium	A74
Q5_K_M	5	19.4 GB	High	A74
Q6_K	6	22.1 GB	High	A75
Q8_0	8	28.9 GB	Very High	A76
F16Best for your GPU	16	55.4 GB	Maximum	A80

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

17.6 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

259 tok/s

Qwen 3.5 122B A10B

122B

52.1 tok/s

Qwen 3.6 35B A3B

35B

217.7 tok/s

Qwen3-VL 30B A3B Instruct

30B

267.8 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Gemma 3 27B?

Yes, NVIDIA A100 80GB can run Gemma 3 27B with a A grade (Runs well). Expected decode speed: 109.2 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 36.9 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 A100 80GB?

On NVIDIA A100 80GB, Gemma 3 27B achieves approximately 109.2 tokens per second decode speed with a time-to-first-token of 1773ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA A100 80GB receives a A grade with 109.2 tok/s and 77K context.

What context window can Gemma 3 27B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Gemma 3 27B can safely use up to 77K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.