Can Granite 4.1 30B run on AMD Instinct MI100 32GB?

YES — Tight Fit

A84Great

Estimated from fit model

Granite 4.1 30B needs ~26.3 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 26.3 GB, 46.9 tok/s, Tight fit

26.3 GB required32.0 GB available

82% VRAM used

Fit status

Tight fit

Decode

46.9 tok/s

TTFT

4129 ms

Safe context

39K

Memory

26.3 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGranite 4.1 30B on AMD Instinct MI100 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: 46.9 tok/s decode · 4.1s TTFT (warm) · 117 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	46.9 tok/s	2252 ms	39K
Coding	A	Tight fit	46.9 tok/s	4129 ms	39K
Agentic Coding	A	Tight fit	46.9 tok/s	6006 ms	39K
Reasoning	A	Tight fit	46.9 tok/s	4880 ms	39K
RAG	A	Tight fit	46.9 tok/s	7507 ms	39K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A79
Q3_K_S	3	14.7 GB	Low	A81
NVFP4	4

Get started

Copy-paste commands to run Granite 4.1 30B on your machine.

Run

ollama run granite4.1:30b

Your hardware

More models your AMD Instinct MI100 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	120.7 tok/s
Qwen 3.6 35B A3B	35B	S	101.4 tok/s

Frequently asked questions

Can AMD Instinct MI100 32GB run Granite 4.1 30B?

Yes, AMD Instinct MI100 32GB can run Granite 4.1 30B with a A grade (Tight fit). Expected decode speed: 46.9 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 30B?

The recommended quantization for Granite 4.1 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 30B run at on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Granite 4.1 30B achieves approximately 46.9 tokens per second decode speed with a time-to-first-token of 4129ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on AMD Instinct MI100 32GB receives a A grade with 46.9 tok/s and 39K context.

What context window can Granite 4.1 30B use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Granite 4.1 30B can safely use up to 39K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI100 32GB See all hardware for Granite 4.1 30B

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