Can Gemma 4 31B run on AMD Instinct MI210 64GB?

YES — Runs Great

S91Excellent

Estimated from fit model

Gemma 4 31B needs ~41.0 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~60 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) — 41.0 GB, 62.5 tok/s, Runs well

41.0 GB required64.0 GB available

64% VRAM used

Fit status

Runs well

Decode

62.5 tok/s

TTFT

3100 ms

Safe context

41K

Memory

41.0 GB / 64.0 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsGemma 4 31B on AMD Instinct MI210 64GB
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: 62.5 tok/s decode · 3.1s TTFT (warm) · 156 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	59.5 tok/s	1775 ms	41K
Coding	S	Runs well	59.5 tok/s	3255 ms	41K
Agentic Coding	S	Tight fit	59.5 tok/s	4735 ms	41K
Reasoning	S	Runs well	59.5 tok/s	3847 ms	41K
RAG	S	Tight fit	59.5 tok/s	5918 ms	41K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A79
Q3_K_S	3	15.0 GB	Low	A79
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 MI210 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	141.5 tok/s

Frequently asked questions

Can AMD Instinct MI210 64GB run Gemma 4 31B?

Yes, AMD Instinct MI210 64GB can run Gemma 4 31B with a S grade (Runs well). Expected decode speed: 59.5 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 41.0 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 MI210 64GB?

On AMD Instinct MI210 64GB, Gemma 4 31B achieves approximately 59.5 tokens per second decode speed with a time-to-first-token of 3255ms using Q4_K_M quantization.

Can AMD Instinct MI210 64GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on AMD Instinct MI210 64GB receives a S grade with 59.5 tok/s and 41K context.

What context window can Gemma 4 31B use on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, Gemma 4 31B can safely use up to 41K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI210 64GB See all hardware for Gemma 4 31B

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