Can MiniMax M2.7 run on NVIDIA B200 180GB?

YES — Tight Fit

S90Excellent

Estimated from fit model

MiniMax M2.7 needs ~163.0 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With UD-IQ4_XS quantization, expect ~143 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced

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) — 494.2 GB, exceeds 180.0 GB available

494.2 GB required180.0 GB available

275% VRAM needed

314.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.4 tok/s

TTFT

18536 ms

Safe context

Memory

494.2 GB / 180.0 GB

Offload

60%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on NVIDIA B200 180GB
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: 10.4 tok/s decode · 18.5s TTFT (warm) · 26 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	155.6 tok/s	679 ms	88K
Coding	S	Tight fit	143.1 tok/s	1353 ms	88K
Agentic Coding	S	Tight fit	155.6 tok/s	1810 ms	88K
Reasoning	S	Tight fit	155.6 tok/s	1471 ms	88K
RAG	S	Tight fit	155.6 tok/s	2262 ms	88K

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

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	89.7 GB	Low	A84
Q3_K_S	3	112.7 GB	Low	A84
NVFP4	4

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 B200 180GB can run

Model	Params	Grade	Decode	Capabilities
DeepSeek V4 Flash	284B	S	144.8 tok/s
Qwen 3 235B A22B	235B	S	136.8 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run MiniMax M2.7?

Yes, NVIDIA B200 180GB can run MiniMax M2.7 with a S grade (Tight fit). Expected decode speed: 143.1 tok/s.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 163.0 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 B200 180GB?

On NVIDIA B200 180GB, MiniMax M2.7 achieves approximately 143.1 tokens per second decode speed with a time-to-first-token of 1353ms using UD-IQ4_XS quantization.

Can NVIDIA B200 180GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on NVIDIA B200 180GB receives a S grade with 143.1 tok/s and 88K context.

What context window can MiniMax M2.7 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, MiniMax M2.7 can safely use up to 88K tokens of context. The model's official context limit is 205K, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for MiniMax M2.7

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