Can MiniMax M2.7 run on NVIDIA H100 80GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

MiniMax M2.7 needs ~153.0 GB but NVIDIA H100 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

4.1 tok/s

TTFT

47553 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 H100 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: 4.1 tok/s decode · 47.6s TTFT (warm) · 10 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	19.5 tok/s	5426 ms	4K
Coding	F	Too heavy	19.1 tok/s	10154 ms	4K
Agentic Coding	F	Too heavy	18.3 tok/s	15377 ms	4K
Reasoning	F	Too heavy	19.1 tok/s	12000 ms	4K
RAG	F	Too heavy	18.3 tok/s	19221 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 H100 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	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

Opciones de mejora

Hardware que ejecuta bien MiniMax M2.7

NVIDIA B200 180GBOpción económica

180 GB VRAM (+100)8000 GB/s (+4650)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.155.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$30,000 MSRP

NVIDIA H200 141GBMejor relación calidad-precio

141 GB VRAM (+61)4800 GB/s (+1450)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.65.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 241%.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejora NVIDIA

141 GB VRAM (+61)4800 GB/s (+1450)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.55.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 192%.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA H100 80GB run MiniMax M2.7?

No, MiniMax M2.7 requires more memory than NVIDIA H100 80GB provides.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 153.0 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 H100 80GB?

On NVIDIA H100 80GB, MiniMax M2.7 achieves approximately 19.1 tokens per second decode speed with a time-to-first-token of 10154ms using UD-IQ4_XS quantization.

Can NVIDIA H100 80GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on NVIDIA H100 80GB receives a F grade with 19.1 tok/s and 4K context.

What context window can MiniMax M2.7 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB?

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 H100 80GB See all hardware for MiniMax M2.7

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