Can Gemma 4 12B run on NVIDIA V100 32GB?

YES — Runs Great

A85Great

Estimated from fit model

Gemma 4 12B needs ~17.6 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~87 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) — 17.6 GB, 86.5 tok/s, Runs well

17.6 GB required32.0 GB available

55% VRAM used

Fit status

Runs well

Decode

86.5 tok/s

TTFT

2238 ms

Safe context

55K

Memory

17.6 GB / 32.0 GB

Memory breakdown

Weights7.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 4 12B 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: 86.5 tok/s decode · 2.2s TTFT (warm) · 216 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	86.5 tok/s	1221 ms	55K
Coding	A	Runs well	86.5 tok/s	2238 ms	55K
Agentic Coding	S	Runs well	86.5 tok/s	3256 ms	55K
Reasoning	A	Runs well	86.5 tok/s	2645 ms	55K
RAG	S	Runs well	86.5 tok/s	4070 ms	55K

Inference speed

Gemma 4 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 12B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~168 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	168.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	99.1	Fits
RTX 3090 24GB	24 GB	Q4_K_M	94.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	87.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	47.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	43.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	34.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	32.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	31.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	26.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	23.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	14.8	Too big
RTX 4060 8GB	8 GB	Q4_K_M	5.5	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 12B (12B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A75
Q3_K_S	3	5.9 GB	Low	A75
NVFP4	4	6.7 GB	Medium	A76
Q4_K_M	4	7.3 GB	Medium	A76
Q5_K_M	5	8.6 GB	High	A77
Q6_K	6	9.8 GB	High	A77
Q8_0	8	12.8 GB	Very High	A79
F16Best for your GPU	16	24.6 GB	Maximum	A80

Get started

Copy-paste commands to run Gemma 4 12B on your machine.

Run

lms load gemma-4-12B-it && lms server start

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 3.5 27B	27B	S	39.5 tok/s
Qwen 3.6 27B	27B	S	39.7 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Gemma 4 12B?

Yes, NVIDIA V100 32GB can run Gemma 4 12B with a A grade (Runs well). Expected decode speed: 86.5 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 17.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 12B?

The recommended quantization for Gemma 4 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 12B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Gemma 4 12B achieves approximately 86.5 tokens per second decode speed with a time-to-first-token of 2238ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on NVIDIA V100 32GB receives a A grade with 86.5 tok/s and 55K context.

What context window can Gemma 4 12B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Gemma 4 12B can safely use up to 55K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Gemma 4 12B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

86.5 tok/s

1221 ms

55K

Coding

Runs well

86.5 tok/s

2238 ms

55K

Agentic Coding

Runs well

86.5 tok/s

3256 ms

55K

Reasoning

Runs well

86.5 tok/s

2645 ms

55K

RAG

Runs well

86.5 tok/s

4070 ms

55K

Gemma 4 12B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

168.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

109.9

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

99.1

Fits

RTX 3090 24GB

24 GB

Q4_K_M

94.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

87.6

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

60.5

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

50.4

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

47.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

43.4

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

34.4

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

32.9

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

31.6

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

26.5

Fits

RTX 4070 12GB

12 GB

Q4_K_M

23.5

Too big

RTX 3060 12GB

12 GB

Q4_K_M

14.8

Too big

RTX 4060 8GB

8 GB

Q4_K_M

5.5

Too big

Quantization options

How Gemma 4 12B (12B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A75
Q3_K_S	3	5.9 GB	Low	A75
NVFP4	4	6.7 GB	Medium	A76
Q4_K_M	4	7.3 GB	Medium	A76
Q5_K_M	5	8.6 GB	High	A77
Q6_K	6	9.8 GB	High	A77
Q8_0	8	12.8 GB	Very High	A79
F16Best for your GPU	16	24.6 GB	Maximum	A80

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

91.2 tok/s

Qwen 3.5 27B

27B

39.5 tok/s

Qwen 3.6 27B

27B

39.7 tok/s

Qwen 3.6 35B A3B

35B

76.6 tok/s

Qwen3-VL 30B A3B Instruct

30B

94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Gemma 4 12B?

Yes, NVIDIA V100 32GB can run Gemma 4 12B with a A grade (Runs well). Expected decode speed: 86.5 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 17.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 12B?

The recommended quantization for Gemma 4 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 12B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Gemma 4 12B achieves approximately 86.5 tokens per second decode speed with a time-to-first-token of 2238ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on NVIDIA V100 32GB receives a A grade with 86.5 tok/s and 55K context.

What context window can Gemma 4 12B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Gemma 4 12B can safely use up to 55K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.