Can MiniMax M2.7 run on NVIDIA A100 40GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

MiniMax M2.7 needs ~149.0 GB but NVIDIA A100 40GB only has 40.0 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) — 480.2 GB, exceeds 40.0 GB available

480.2 GB required40.0 GB available

1201% VRAM needed

440.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

480.2 GB / 40.0 GB

Offload

90%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom4.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on NVIDIA A100 40GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 149.0 GB, but this setup only exposes 40.0 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	4.5 tok/s	23278 ms	4K
Coding	F	Too heavy	4.5 tok/s	42677 ms	4K
Agentic Coding	F	Too heavy	4.5 tok/s	62076 ms	4K
Reasoning	F	Too heavy	4.5 tok/s	50437 ms	4K
RAG	F	Too heavy	4.5 tok/s	77595 ms	4K

Inference speed

MiniMax M2.7 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for MiniMax M2.7 at UD-IQ4_XS across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~20 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	UD-IQ4_XS	20.0	Tight
Mac Studio M2 Ultra 128GB	128 GB	UD-IQ4_XS	8.3	Too big
Mac Studio M1 Ultra 128GB	128 GB	UD-IQ4_XS	7.9	Too big
MacBook Pro M4 Max 128GB	128 GB	UD-IQ4_XS	6.2	Too big
MacBook Pro M4 Max 64GB	64 GB	UD-IQ4_XS	5.6	Too big
RTX 5090 32GB	32 GB	UD-IQ4_XS	4.2	Too big
2× RX 7900 XTX 24GB	48 GB	UD-IQ4_XS	4.1	Too big
MacBook Pro M3 Max 64GB	64 GB	UD-IQ4_XS	3.9	Too big
MacBook Pro M1 Max 64GB	64 GB	UD-IQ4_XS	3.6	Too big
MacBook Pro M4 Pro 48GB	48 GB	UD-IQ4_XS	3.4	Too big
2× RTX 4090 24GB	48 GB	UD-IQ4_XS	2.8	Too big
RTX 4090 24GB	24 GB	UD-IQ4_XS	2.7	Too big
RX 7900 XTX 24GB	24 GB	UD-IQ4_XS	2.4	Too big
2× RTX 3090 24GB	48 GB	UD-IQ4_XS	2.4	Too big
RTX 3090 24GB	24 GB	UD-IQ4_XS	2.3	Too big
RTX 4080 Super 16GB	16 GB	UD-IQ4_XS	2.1	Too big
4× RTX 3060 12GB	48 GB	UD-IQ4_XS	2.1	Too big
RTX 4070 12GB	12 GB	UD-IQ4_XS	2.0	Too big
RTX 3060 12GB	12 GB	UD-IQ4_XS	2.0	Too big
RTX 4060 8GB	8 GB	UD-IQ4_XS	2.0	Too big

Estimates for single-stream decoding at UD-IQ4_XS; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on NVIDIA A100 40GB (40.0 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	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

Upgrade options

Hardware that runs MiniMax M2.7 well

NVIDIA B200 180GBBudget pick

180 GB VRAM (+140)8000 GB/s (+6445)

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 H200 141GBBest value

141 GB VRAM (+101)4800 GB/s (+3245)

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 1349%.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBNVIDIA upgrade

141 GB VRAM (+101)4800 GB/s (+3245)

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 1140%.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run MiniMax M2.7?

No, MiniMax M2.7 requires more memory than NVIDIA A100 40GB provides.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 149.0 GB of memory with UD-IQ4_XS quantization.

What is the best quantization for MiniMax M2.7?

The recommended quantization for MiniMax M2.7 is UD-IQ4_XS, which balances quality and memory efficiency.

What speed will MiniMax M2.7 run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, MiniMax M2.7 achieves approximately 4.5 tokens per second decode speed with a time-to-first-token of 42677ms using UD-IQ4_XS quantization.

Can NVIDIA A100 40GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on NVIDIA A100 40GB receives a F grade with 4.5 tok/s and 4K context.

What context window can MiniMax M2.7 use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, MiniMax M2.7 can safely use up to 4K tokens of context. The model's official context limit is 205K, but available memory constrains the safe maximum.

What should I upgrade first if MiniMax M2.7 feels slow on NVIDIA A100 40GB?

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.

See all results for NVIDIA A100 40GB See all hardware for MiniMax M2.7

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

4.5 tok/s

23278 ms

Coding

Too heavy

4.5 tok/s

42677 ms

Agentic Coding

Too heavy

4.5 tok/s

62076 ms

Reasoning

Too heavy

4.5 tok/s

50437 ms

RAG

Too heavy

4.5 tok/s

77595 ms

MiniMax M2.7 inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

UD-IQ4_XS

20.0

Tight

Mac Studio M2 Ultra 128GB

128 GB

UD-IQ4_XS

8.3

Too big

Mac Studio M1 Ultra 128GB

128 GB

UD-IQ4_XS

7.9

Too big

MacBook Pro M4 Max 128GB

128 GB

UD-IQ4_XS

6.2

Too big

MacBook Pro M4 Max 64GB

64 GB

UD-IQ4_XS

5.6

Too big

RTX 5090 32GB

32 GB

UD-IQ4_XS

4.2

Too big

2× RX 7900 XTX 24GB

48 GB

UD-IQ4_XS

4.1

Too big

MacBook Pro M3 Max 64GB

64 GB

UD-IQ4_XS

3.9

Too big

MacBook Pro M1 Max 64GB

64 GB

UD-IQ4_XS

3.6

Too big

MacBook Pro M4 Pro 48GB

48 GB

UD-IQ4_XS

3.4

Too big

2× RTX 4090 24GB

48 GB

UD-IQ4_XS

2.8

Too big

RTX 4090 24GB

24 GB

UD-IQ4_XS

2.7

Too big

RX 7900 XTX 24GB

24 GB

UD-IQ4_XS

2.4

Too big

2× RTX 3090 24GB

48 GB

UD-IQ4_XS

2.4

Too big

RTX 3090 24GB

24 GB

UD-IQ4_XS

2.3

Too big

RTX 4080 Super 16GB

16 GB

UD-IQ4_XS

2.1

Too big

4× RTX 3060 12GB

48 GB

UD-IQ4_XS

2.1

Too big

RTX 4070 12GB

12 GB

UD-IQ4_XS

2.0

Too big

RTX 3060 12GB

12 GB

UD-IQ4_XS

2.0

Too big

RTX 4060 8GB

8 GB

UD-IQ4_XS

2.0

Too big

Quant

Bits

VRAM

Quality

Fit

Q2_K

89.7 GB

Low

Q3_K_S

112.7 GB

Low

NVFP4

128.8 GB

Medium

Q4_K_M

140.3 GB

Medium

Q5_K_M

165.6 GB

High

Q6_K

188.6 GB

High

Q8_0

246.1 GB

Very High

F16

471.5 GB

Maximum

Frequently asked questions

Can NVIDIA A100 40GB run MiniMax M2.7?

No, MiniMax M2.7 requires more memory than NVIDIA A100 40GB provides.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 149.0 GB of memory with UD-IQ4_XS quantization.

What is the best quantization for MiniMax M2.7?

The recommended quantization for MiniMax M2.7 is UD-IQ4_XS, which balances quality and memory efficiency.

What speed will MiniMax M2.7 run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, MiniMax M2.7 achieves approximately 4.5 tokens per second decode speed with a time-to-first-token of 42677ms using UD-IQ4_XS quantization.

Can NVIDIA A100 40GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on NVIDIA A100 40GB receives a F grade with 4.5 tok/s and 4K context.

What context window can MiniMax M2.7 use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, MiniMax M2.7 can safely use up to 4K tokens of context. The model's official context limit is 205K, but available memory constrains the safe maximum.

What should I upgrade first if MiniMax M2.7 feels slow on NVIDIA A100 40GB?

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.