Can MiniMax M2.7 run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — With Q2_K

A76Great

Estimated from fit model

MiniMax M2.7 needs ~104.0 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q2_K quantization, expect ~27 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.

MiniMax M2.7 at UD-IQ4_XS needs 154.6 GB — too much for RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB). Runs at Q2_K (104.0 GB) with low quality.

Capabilities:

Select quantization to explore

F16 (Maximum quality) — 485.8 GB, exceeds 96.0 GB available

485.8 GB required96.0 GB available

506% VRAM needed

389.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

485.8 GB / 96.0 GB

Offload

80%

Memory breakdown

Weights471.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMiniMax M2.7 on RTX PRO 6000 Blackwell Server Edition 96GB
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

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 6.9 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	F	Too heavy	9.2 tok/s	11459 ms	4K
Coding	F	Too heavy	9.0 tok/s	21542 ms	4K
Agentic Coding	F	Too heavy	8.6 tok/s	32918 ms	4K
Reasoning	F	Too heavy	9.0 tok/s	25458 ms	4K
RAG	F	Too heavy	8.6 tok/s	41148 ms	4K

Quantization options

How MiniMax M2.7 (230B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 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

Get started

Copy-paste commands to run MiniMax M2.7 on your machine.

Run

lms load MiniMax-M2.7 && lms server start

Upgrade-Optionen

Hardware, die MiniMax M2.7 gut ausführt

NVIDIA B200 180GBGünstige Wahl

180 GB VRAM (+84)8000 GB/s (+6403)

Makes the model fit on the accelerator instead of staying completely out of reach.155.6 tok/s Dekodierung

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.

ca. $30,000 MSRP

NVIDIA H200 141GBBestes Preis-Leistungs-Verhältnis

141 GB VRAM (+45)4800 GB/s (+3203)

Makes the model fit on the accelerator instead of staying completely out of reach.65.2 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 624%.

ca. $30,000 MSRP

NVIDIA H200 PCIe 141GBNVIDIA-Upgrade

141 GB VRAM (+45)4800 GB/s (+3203)

Makes the model fit on the accelerator instead of staying completely out of reach.55.8 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 520%.

ca. $30,000 MSRP

Frequently asked questions

Can RTX PRO 6000 Blackwell Server Edition 96GB run MiniMax M2.7?

Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run MiniMax M2.7 at Q2_K quantization (Very compromised (needs ~6.9 GB host RAM)). The recommended UD-IQ4_XS requires 154.6 GB which exceeds available memory, but at Q2_K it needs only 104.0 GB. Expected decode speed: 26.8 tok/s.

How much VRAM does MiniMax M2.7 need?

MiniMax M2.7 (230B parameters) requires approximately 154.6 GB at UD-IQ4_XS quantization. On RTX PRO 6000 Blackwell Server Edition 96GB, it fits at Q2_K using 104.0 GB.

What is the best quantization for MiniMax M2.7?

The recommended quantization is UD-IQ4_XS, but on RTX PRO 6000 Blackwell Server Edition 96GB the best fitting quantization is Q2_K, which uses 104.0 GB.

What speed will MiniMax M2.7 run at on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, MiniMax M2.7 achieves approximately 26.8 tokens per second decode speed with a time-to-first-token of 7216ms using Q2_K quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run MiniMax M2.7 for coding?

For coding workloads, MiniMax M2.7 on RTX PRO 6000 Blackwell Server Edition 96GB receives a F grade with 9.0 tok/s and 4K context.

What context window can MiniMax M2.7 use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, MiniMax M2.7 can safely use up to 4K tokens of context at Q2_K quantization. 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 RTX PRO 6000 Blackwell Server Edition 96GB?

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.

See all results for RTX PRO 6000 Blackwell Server Edition 96GB See all hardware for MiniMax M2.7

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