Can MiniMax M2.7 run on AMD Instinct MI250X 128GB?

Q: What should I upgrade first if MiniMax M2.7 feels slow on AMD Instinct MI250X 128GB?

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.

YES — With NVFP4

A77Great

Estimated from fit model

MiniMax M2.7 needs ~146.3 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With NVFP4 quantization, expect ~37 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.

MiniMax M2.7 at UD-IQ4_XS needs 157.8 GB — too much for AMD Instinct MI250X 128GB (128.0 GB). Runs at NVFP4 (146.3 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

F16 (Maximum quality) — 489.0 GB, exceeds 128.0 GB available

489.0 GB required128.0 GB available

382% VRAM needed

361.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.6 tok/s

TTFT

53611 ms

Safe context

Memory

489.0 GB / 128.0 GB

Offload

70%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on AMD Instinct MI250X 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: 3.6 tok/s decode · 53.6s TTFT (warm) · 9 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 16.1 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	F	Too heavy	28.6 tok/s	3689 ms	4K
Coding	F	Too heavy	27.9 tok/s	6938 ms	4K
Agentic Coding	F	Too heavy	26.5 tok/s	10608 ms	4K
Reasoning	F	Too heavy	27.9 tok/s	8200 ms	4K
RAG	F	Too heavy	26.5 tok/s	13260 ms	4K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	89.7 GB	Low	A84
Q3_K_S	3	112.7 GB	Low	F0

Get started

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

Run

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

Upgrade options

Hardware that runs MiniMax M2.7 well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+160)8000 GB/s (+4800)

Makes the model fit on the accelerator instead of staying completely out of reach.135.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

AMD Instinct MI300X 192GBBest value

192 GB VRAM (+64)5300 GB/s (+2100)

Makes the model fit on the accelerator instead of staying completely out of reach.95.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

AMD Instinct MI325X 256GBAMD upgrade

256 GB VRAM (+128)6000 GB/s (+2800)

Makes the model fit on the accelerator instead of staying completely out of reach.101.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$20,000 MSRP

Frequently asked questions

Can AMD Instinct MI250X 128GB run MiniMax M2.7?

Yes, AMD Instinct MI250X 128GB can run MiniMax M2.7 at NVFP4 quantization (Very compromised (needs ~16.1 GB host RAM)). The recommended UD-IQ4_XS requires 157.8 GB which exceeds available memory, but at NVFP4 it needs only 146.3 GB. Expected decode speed: 37.4 tok/s.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 157.8 GB at UD-IQ4_XS quantization. On AMD Instinct MI250X 128GB, it fits at NVFP4 using 146.3 GB.

What is the best quantization for MiniMax M2.7?

The recommended quantization is UD-IQ4_XS, but on AMD Instinct MI250X 128GB the best fitting quantization is NVFP4, which uses 146.3 GB.

What speed will MiniMax M2.7 run at on AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, MiniMax M2.7 achieves approximately 37.4 tokens per second decode speed with a time-to-first-token of 5173ms using NVFP4 quantization.

Can AMD Instinct MI250X 128GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on AMD Instinct MI250X 128GB receives a F grade with 27.9 tok/s and 4K context.

What context window can MiniMax M2.7 use on AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, MiniMax M2.7 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 205K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI250X 128GB See all hardware for MiniMax M2.7

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