Can Gemma 4 12B run on Tesla P40 24GB?

YES — Runs Great

A85Great

Estimated from fit model

Gemma 4 12B needs ~16.8 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 16.8 GB, 29.3 tok/s, Runs well

16.8 GB required24.0 GB available

70% VRAM used

Fit status

Runs well

Decode

29.3 tok/s

TTFT

6611 ms

Safe context

36K

Memory

16.8 GB / 24.0 GB

Memory breakdown

Weights7.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 4 12B on Tesla P40 24GB
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: 29.3 tok/s decode · 6.6s TTFT (warm) · 73 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	29.3 tok/s	3606 ms	36K
Coding	A	Runs well	29.3 tok/s	6611 ms	36K
Agentic Coding	A	Tight fit	29.3 tok/s	9617 ms	36K
Reasoning	A	Runs well	29.3 tok/s	7814 ms	36K
RAG	A	Tight fit	29.3 tok/s	12021 ms	36K

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 Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A77
Q3_K_S	3	5.9 GB	Low	A77
NVFP4	4	6.7 GB	Medium	A78
Q4_K_M	4	7.3 GB	Medium	A78
Q5_K_M	5	8.6 GB	High	A79
Q6_K	6	9.8 GB	High	A80
Q8_0Best for your GPU	8	12.8 GB	Very High	A82
F16	16	24.6 GB	Maximum	F0

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 Tesla P40 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
Qwen 3.5 27B	27B	S	13.4 tok/s
Qwen 3.6 27B	27B	S	13.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	31.9 tok/s
Qwen 3.5 35B A3B	35B	A	16.7 tok/s

Frequently asked questions

Can Tesla P40 24GB run Gemma 4 12B?

Yes, Tesla P40 24GB can run Gemma 4 12B with a A grade (Runs well). Expected decode speed: 29.3 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 16.8 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 Tesla P40 24GB?

On Tesla P40 24GB, Gemma 4 12B achieves approximately 29.3 tokens per second decode speed with a time-to-first-token of 6611ms using Q4_K_M quantization.

Can Tesla P40 24GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on Tesla P40 24GB receives a A grade with 29.3 tok/s and 36K context.

What context window can Gemma 4 12B use on Tesla P40 24GB?

On Tesla P40 24GB, Gemma 4 12B can safely use up to 36K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Gemma 4 12B

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