Can MiniMax M2.7 run on NVIDIA H200 141GB?

BARELY — Tight on Memory

A79Great

○Estimated from fit model

MiniMax M2.7 needs ~159.1 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With UD-IQ4_XS quantization, expect ~65 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.

Capabilities:

Select quantization to explore

F16 (Maximum quality) — 490.3 GB, exceeds 141.0 GB available

490.3 GB required141.0 GB available

348% VRAM needed

349.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.8 tok/s

TTFT

33188 ms

Safe context

Memory

490.3 GB / 141.0 GB

Offload

70%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on NVIDIA H200 141GB
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: 5.8 tok/s decode · 33.2s TTFT (warm) · 15 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 15.9 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	A	Very compromised (needs ~14.5 GB host RAM)	66.6 tok/s	1587 ms	4K
Coding	A	Very compromised (needs ~15.9 GB host RAM)	65.2 tok/s	2967 ms	4K
Agentic Coding	A	Very compromised (needs ~18.8 GB host RAM)	62.8 tok/s	4486 ms	4K
Reasoning	A	Very compromised (needs ~15.9 GB host RAM)	65.2 tok/s	3507 ms	4K
RAG	A	Very compromised (needs ~18.8 GB host RAM)	62.8 tok/s	5608 ms	4K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on NVIDIA H200 141GB (141.0 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
NVFP4	4	128.8 GB	Medium	F0
Q4_K_M	4	140.3 GB	Medium	F0
Q5_K_M	5	165.6 GB	High	F0
Q6_K	6	188.6 GB	High	F0
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 NVIDIA H200 141GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3 235B A22B	235B	A	56.1 tok/s

Frequently asked questions

See all results for NVIDIA H200 141GB See all hardware for MiniMax M2.7

URL: https://willitrunai.com/can-run/minimax-m2-7-on-h200-141gb

⇱ MiniMax M2.7 on NVIDIA H200 141GB? YES