Can Gemma 4 31B run on RTX PRO 4500 Blackwell 32GB?

BARELY — Tight on Memory

A75Great

Estimated from fit model

Gemma 4 31B needs ~37.5 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 37.5 GB, 16.2 tok/s, Very compromised (needs ~2.7 GB host RAM)

37.5 GB required32.0 GB available

117% VRAM needed

5.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.7 GB host RAM)

Decode

16.2 tok/s

TTFT

11963 ms

Safe context

10K

Memory

37.5 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 4 31B on RTX PRO 4500 Blackwell 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: 16.2 tok/s decode · 12.0s TTFT (warm) · 41 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	29.2 tok/s	3615 ms	10K
Coding	A	Very compromised (needs ~2.7 GB host RAM)	16.2 tok/s	11963 ms	10K
Agentic Coding	F	Too heavy	8.3 tok/s	34109 ms	10K
Reasoning	A	Very compromised (needs ~2.7 GB host RAM)	16.2 tok/s	14138 ms	10K
RAG	F	Too heavy	8.3 tok/s	42636 ms	10K

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 PRO 4500 Blackwell 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A84
Q3_K_S	3	15.0 GB	Low	S86
NVFP4	4	17.2 GB	Medium	S86
Q4_K_M	4	18.7 GB	Medium	S86
Q5_K_M	5	22.1 GB	High	S86
Q6_KBest for your GPU	6	25.2 GB	High	S85
Q8_0	8	32.8 GB	Very High	F0
F16	16	62.9 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 31B on your machine.

Run

ollama run gemma4:31b

Your hardware

More models your RTX PRO 4500 Blackwell 32GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	95.6 tok/s
Qwen 3.5 35B A3B	35B	S	104 tok/s
Qwen 3 32B	32B	S	41.9 tok/s

Frequently asked questions

Can RTX PRO 4500 Blackwell 32GB run Gemma 4 31B?

Yes, RTX PRO 4500 Blackwell 32GB can run Gemma 4 31B with a A grade (Very compromised (needs ~2.7 GB host RAM)). Expected decode speed: 16.2 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 37.5 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 PRO 4500 Blackwell 32GB?

On RTX PRO 4500 Blackwell 32GB, Gemma 4 31B achieves approximately 16.2 tokens per second decode speed with a time-to-first-token of 11963ms using Q4_K_M quantization.

Can RTX PRO 4500 Blackwell 32GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on RTX PRO 4500 Blackwell 32GB receives a A grade with 16.2 tok/s and 10K context.

What context window can Gemma 4 31B use on RTX PRO 4500 Blackwell 32GB?

On RTX PRO 4500 Blackwell 32GB, Gemma 4 31B can safely use up to 10K 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 PRO 4500 Blackwell 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX PRO 4500 Blackwell 32GB See all hardware for Gemma 4 31B

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