Can MiniMax M2.7 run on NVIDIA GH200 96GB?

YES — With Q2_K

A80Great

○Estimated from fit model

MiniMax M2.7 needs ~104.0 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q2_K quantization, expect ~75 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 154.6 GB — too much for NVIDIA GH200 96GB (96.0 GB). Runs at Q2_K (104.0 GB) with low quality.

Capabilities:

Select quantization to explore

F16 (Maximum quality) — 485.8 GB, exceeds 96.0 GB available

485.8 GB required96.0 GB available

506% VRAM needed

389.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.7 tok/s

TTFT

41301 ms

Safe context

Memory

485.8 GB / 96.0 GB

Offload

80%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on NVIDIA GH200 96GB
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: 4.7 tok/s decode · 41.3s TTFT (warm) · 12 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 6.9 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	27.4 tok/s	3860 ms	4K
Coding	F	Too heavy	26.8 tok/s	7222 ms	4K
Agentic Coding	F	Too heavy	25.8 tok/s	10932 ms	4K
Reasoning	F	Too heavy	26.8 tok/s	8535 ms	4K
RAG	F	Too heavy	25.8 tok/s	13665 ms	4K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	89.7 GB	Low	F0
Q3_K_S	3	112.7 GB	Low	F0
NVFP4	4

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

👁 NVIDIA
NVIDIA B200 180GBBudget pick

180 GB VRAM (+84)8000 GB/s (+4000)

Makes the model fit on the accelerator instead of staying completely out of reach.155.6 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.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 141GBBest value

141 GB VRAM (+45)4800 GB/s (+800)

Makes the model fit on the accelerator instead of staying completely out of reach.65.2 tok/s decode

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

Raises estimated decode speed by about 123%.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBNVIDIA upgrade

141 GB VRAM (+45)4800 GB/s (+800)

Makes the model fit on the accelerator instead of staying completely out of reach.55.8 tok/s decode

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

Raises estimated decode speed by about 91%.

~$30,000 MSRP

Frequently asked questions

See all results for NVIDIA GH200 96GB See all hardware for MiniMax M2.7

URL: https://willitrunai.com/can-run/minimax-m2-7-on-gh200-96gb

⇱

Can MiniMax M2.7 run on NVIDIA GH200 96GB?

YES — With Q2_K

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