Can MiniMax M2.7 run on NVIDIA A800 80GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

MiniMax M2.7 needs ~153.0 GB but NVIDIA A800 80GB only has 80.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity

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) — 484.2 GB, exceeds 80.0 GB available

484.2 GB required80.0 GB available

605% VRAM needed

404.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.2 tok/s

TTFT

88660 ms

Safe context

Memory

484.2 GB / 80.0 GB

Offload

80%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on NVIDIA A800 80GB
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: 2.2 tok/s decode · 88.7s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 153.0 GB, but this setup only exposes 80.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	9.6 tok/s	11001 ms	4K
Coding	F	Too heavy	9.4 tok/s	20587 ms	4K
Agentic Coding	F	Too heavy	9.0 tok/s	31177 ms	4K
Reasoning	F	Too heavy	9.4 tok/s	24330 ms	4K
RAG	F	Too heavy	9.0 tok/s	38971 ms	4K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on NVIDIA A800 80GB (80.0 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

Upgrade options

Hardware that runs MiniMax M2.7 well

👁 NVIDIA
NVIDIA B200 180GBBudget pick

180 GB VRAM (+100)8000 GB/s (+6065)

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 (+61)4800 GB/s (+2865)

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 539%.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBNVIDIA upgrade

141 GB VRAM (+61)4800 GB/s (+2865)

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 447%.

~$30,000 MSRP

Frequently asked questions

See all results for NVIDIA A800 80GB See all hardware for MiniMax M2.7

URL: https://willitrunai.com/can-run/minimax-m2-7-on-a800-80gb

⇱

Can MiniMax M2.7 run on NVIDIA A800 80GB?

NO — Won't Fit

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

Hardware that runs MiniMax M2.7 well

Frequently asked questions