Can MiniMax M2.7 run on AMD Instinct MI350X 288GB?

YES — Runs Great

S91Excellent

Estimated from fit model

MiniMax M2.7 needs ~173.8 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With UD-IQ4_XS quantization, expect ~135 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

F16 (Maximum quality) — 505.0 GB, exceeds 288.0 GB available

505.0 GB required288.0 GB available

175% VRAM needed

217.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.0 tok/s

TTFT

14945 ms

Safe context

Memory

505.0 GB / 288.0 GB

Offload

40%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on AMD Instinct MI350X 288GB
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: 13.0 tok/s decode · 14.9s TTFT (warm) · 32 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	135.2 tok/s	781 ms	205K
Coding	S	Runs well	135.2 tok/s	1432 ms	205K
Agentic Coding	S	Runs well	135.2 tok/s	2083 ms	205K
Reasoning	S	Runs well	135.2 tok/s	1692 ms	205K
RAG	S	Runs well	135.2 tok/s	2603 ms	205K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	89.7 GB	Low	A79
Q3_K_S	3	112.7 GB	Low	A81
NVFP4	4	128.8 GB	Medium	A82
Q4_K_M	4	140.3 GB	Medium	A83
Q5_K_M	5	165.6 GB	High	A84
Q6_KBest for your GPU	6	188.6 GB	High	A84
Q8_0	8	246.1 GB	Very High	F0
F16	16	471.5 GB	Maximum	F0

Get started

Copy-paste commands to run MiniMax M2.7 on your machine.

Run

lms load MiniMax-M2.7 && lms server start

Your hardware

More models your AMD Instinct MI350X 288GB can run

Model	Params	Grade	Decode
Qwen 3.5 397B A17B	397B	S	78.9 tok/s
DeepSeek V4 Flash	284B	S	125.8 tok/s
Qwen 3 235B A22B	235B	S	118.9 tok/s
Qwen3-Coder 480B A35B Instruct	480B	A	35.3 tok/s

Frequently asked questions

Can AMD Instinct MI350X 288GB run MiniMax M2.7?

Yes, AMD Instinct MI350X 288GB can run MiniMax M2.7 with a S grade (Runs well). Expected decode speed: 135.2 tok/s.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 173.8 GB of memory with UD-IQ4_XS quantization.

What is the best quantization for MiniMax M2.7?

The recommended quantization for MiniMax M2.7 is UD-IQ4_XS, which balances quality and memory efficiency.

What speed will MiniMax M2.7 run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, MiniMax M2.7 achieves approximately 135.2 tokens per second decode speed with a time-to-first-token of 1432ms using UD-IQ4_XS quantization.

Can AMD Instinct MI350X 288GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on AMD Instinct MI350X 288GB receives a S grade with 135.2 tok/s and 205K context.

What context window can MiniMax M2.7 use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, MiniMax M2.7 can safely use up to 205K tokens of context. The model's official context limit is 205K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for MiniMax M2.7

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