Can MiniMax M2.7 run on AMD Instinct MI300A 128GB?

YES — With NVFP4

A79Great

○Estimated from fit model

MiniMax M2.7 needs ~146.3 GB VRAM. AMD Instinct MI300A 128GB has 128.0 GB. With NVFP4 quantization, expect ~56 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 MI300A 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

5.4 tok/s

TTFT

36068 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 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: 5.4 tok/s decode · 36.1s TTFT (warm) · 13 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	39.1 tok/s	2699 ms	4K
Coding	F	Too heavy	38.1 tok/s	5076 ms	4K
Agentic Coding	F	Too heavy	36.3 tok/s	7761 ms	4K
Reasoning	F	Too heavy	38.1 tok/s	5999 ms	4K
RAG	F	Too heavy	36.3 tok/s	9702 ms	4K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on AMD Instinct MI300A 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 (+2700)

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)

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 (+700)

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

See all results for AMD Instinct MI300A 128GB See all hardware for MiniMax M2.7

URL: https://willitrunai.com/can-run/minimax-m2-7-on-instinct-mi300a-128gb

⇱

Can MiniMax M2.7 run on AMD Instinct MI300A 128GB?

YES — With NVFP4

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs MiniMax M2.7 well

Frequently asked questions