Can Gemma 4 12B run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

A82Great

Estimated from fit model

Gemma 4 12B needs ~19.2 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~162 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) — 19.2 GB, 161.9 tok/s, Runs well

19.2 GB required48.0 GB available

40% VRAM used

Fit status

Runs well

Decode

161.9 tok/s

TTFT

1195 ms

Safe context

95K

Memory

19.2 GB / 48.0 GB

Memory breakdown

Weights7.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGemma 4 12B on RTX PRO 5000 Blackwell 48GB
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: 161.9 tok/s decode · 1.2s TTFT (warm) · 405 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	161.9 tok/s	652 ms	95K
Coding	A	Runs well	161.9 tok/s	1195 ms	95K
Agentic Coding	A	Runs well	161.9 tok/s	1739 ms	95K
Reasoning	A	Runs well	161.9 tok/s	1413 ms	95K
RAG	A	Runs well	161.9 tok/s	2174 ms	95K

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 RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A73
Q3_K_S	3	5.9 GB	Low	A73
NVFP4	4	6.7 GB	Medium	A73
Q4_K_M	4	7.3 GB	Medium	A74
Q5_K_M	5	8.6 GB	High	A74
Q6_K	6	9.8 GB	High	A74
Q8_0	8	12.8 GB	Very High	A75
F16Best for your GPU	16	24.6 GB	Maximum	A79

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 RTX PRO 5000 Blackwell 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	170.7 tok/s
Qwen 3.5 27B	27B	S	74 tok/s
Qwen 3.6 27B	27B	S	74.3 tok/s
Qwen 3.6 35B A3B	35B	S	143.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	176.6 tok/s

Frequently asked questions

Can RTX PRO 5000 Blackwell 48GB run Gemma 4 12B?

Yes, RTX PRO 5000 Blackwell 48GB can run Gemma 4 12B with a A grade (Runs well). Expected decode speed: 161.9 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 19.2 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 RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Gemma 4 12B achieves approximately 161.9 tokens per second decode speed with a time-to-first-token of 1195ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on RTX PRO 5000 Blackwell 48GB receives a A grade with 161.9 tok/s and 95K context.

What context window can Gemma 4 12B use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, Gemma 4 12B can safely use up to 95K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for Gemma 4 12B

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