Can Gemma 4 31B run on RTX 4090 Laptop 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 4 31B needs ~36.2 GB but RTX 4090 Laptop 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 36.2 GB, exceeds 16.0 GB available

36.2 GB required16.0 GB available

226% VRAM needed

20.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.9 tok/s

TTFT

49955 ms

Safe context

Memory

36.2 GB / 16.0 GB

Offload

60%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 31B on RTX 4090 Laptop 16GB
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: 3.9 tok/s decode · 50.0s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 36.2 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.6 tok/s	18851 ms	4K
Coding	F	Too heavy	3.9 tok/s	49955 ms	4K
Agentic Coding	F	Too heavy	3.9 tok/s	72662 ms	4K
Reasoning	F	Too heavy	3.9 tok/s	59038 ms	4K
RAG	F	Too heavy	3.9 tok/s	90828 ms	4K

Inference speed

Gemma 4 31B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 31B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~26 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?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	25.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	25.5	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	23.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	19.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	18.7	Fits
RTX 5090 32GB	32 GB	Q4_K_M	15.0	Heavy offload
RTX 4090 24GB	24 GB	Q4_K_M	13.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	13.0	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	11.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	10.2	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	9.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.8	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	5.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	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 4 31B (30.700000762939453B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	F0
Q3_K_S	3	15.0 GB	Low	F0
NVFP4	4	17.2 GB	Medium	F0
Q4_K_M	4	18.7 GB	Medium	F0
Q5_K_M	5	22.1 GB	High	F0
Q6_K	6	25.2 GB	High	F0
Q8_0	8	32.8 GB	Very High	F0
F16	16	62.9 GB	Maximum	F0

Opções de upgrade

Hardware que roda bem Gemma 4 31B

RTX 5090 32GBMelhor custo-benefício

32 GB VRAM (+16)1792 GB/s (+1216)

Makes the model fit on the accelerator instead of staying completely out of reach.36.7 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 841%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBUpgrade NVIDIA

32 GB VRAM (+16)896 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.23 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 490%.

~$2,499 MSRP

RTX A6000 48GBOpção econômica

48 GB VRAM (+32)768 GB/s (+192)

Makes the model fit on the accelerator instead of staying completely out of reach.32.7 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,650 MSRP

Frequently asked questions

Can RTX 4090 Laptop 16GB run Gemma 4 31B?

No, Gemma 4 31B requires more memory than RTX 4090 Laptop 16GB provides.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 36.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 4 31B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49955ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RTX 4090 Laptop 16GB receives a F grade with 3.9 tok/s and 4K context.

What context window can Gemma 4 31B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 4 31B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 31B feels slow on RTX 4090 Laptop 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 4090 Laptop 16GB See all hardware for Gemma 4 31B

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