Can Pixtral Large 124B run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — With Offload

S88Excellent

○Estimated from fit model

Pixtral Large 124B needs ~91.5 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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

Q4_K_M (Medium quality) — 91.5 GB, 19.3 tok/s, Runs with offload

91.5 GB required96.0 GB available

95% VRAM used

Fit status

Runs with offload

Decode

19.3 tok/s

TTFT

10038 ms

Safe context

29K

Memory

91.5 GB / 96.0 GB

Memory breakdown

Weights75.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsPixtral Large 124B on RTX PRO 6000 Blackwell Server Edition 96GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 19.3 tok/s decode · 10.0s TTFT (warm) · 48 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	19.3 tok/s	5475 ms	29K
Coding	S	Runs with offload	19.3 tok/s	10038 ms	29K
Agentic Coding	S	Runs with offload (needs ~0.7 GB host RAM)	14.5 tok/s	19453 ms	29K
Reasoning	S	Runs with offload	19.3 tok/s	11863 ms	29K
RAG	S	Runs with offload (needs ~0.7 GB host RAM)	14.5 tok/s	24316 ms

Quantization options

How Pixtral Large 124B (124B 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	48.4 GB	Low	S87
Q3_K_S	3	60.8 GB	Low	S87
NVFP4	4

Get started

Copy-paste commands to run Pixtral Large 124B on your machine.

Run

lms load Pixtral-Large-Instruct-2411 && lms server start

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Server Edition 96GB See all hardware for Pixtral Large 124B

URL: https://willitrunai.com/can-run/pixtral-large-124b-on-rtx-pro-6000-blackwell-server-96gb

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