Can Gemma 4 31B run on AMD Instinct MI300A 128GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Gemma 4 31B needs ~47.1 GB VRAM. AMD Instinct MI300A 128GB has 128.0 GB. With Q4_K_M quantization, expect ~123 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 47.1 GB, 129.3 tok/s, Runs well

47.1 GB required128.0 GB available

37% VRAM used

Fit status

Runs well

Decode

129.3 tok/s

TTFT

1498 ms

Safe context

104K

Memory

47.1 GB / 128.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsGemma 4 31B on AMD Instinct MI300A 128GB
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: 129.3 tok/s decode · 1.5s TTFT (warm) · 323 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	S	Runs well	123.1 tok/s	858 ms	104K
Coding	S	Runs well	123.1 tok/s	1573 ms	104K
Agentic Coding	S	Runs well	129.3 tok/s	2178 ms	104K
Reasoning	S	Runs well	129.3 tok/s	1770 ms	104K
RAG	S	Runs well	129.3 tok/s	2723 ms	104K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on AMD Instinct MI300A 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A75
Q3_K_S	3	15.0 GB	Low	A76
NVFP4	4

Get started

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

Run

ollama run gemma4:31b

Your hardware

More models your AMD Instinct MI300A 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	53.8 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can AMD Instinct MI300A 128GB run Gemma 4 31B?

Yes, AMD Instinct MI300A 128GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 123.1 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 47.1 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 AMD Instinct MI300A 128GB?

On AMD Instinct MI300A 128GB, Gemma 4 31B achieves approximately 123.1 tokens per second decode speed with a time-to-first-token of 1573ms using Q4_K_M quantization.

Can AMD Instinct MI300A 128GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on AMD Instinct MI300A 128GB receives a S grade with 123.1 tok/s and 104K context.

What context window can Gemma 4 31B use on AMD Instinct MI300A 128GB?

On AMD Instinct MI300A 128GB, Gemma 4 31B can safely use up to 104K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI300A 128GB See all hardware for Gemma 4 31B

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