Can Pixtral Large 124B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

S95Excellent

○Estimated from fit model

Pixtral Large 124B needs ~96.0 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 96.0 GB, 58.0 tok/s, Runs well

96.0 GB required141.0 GB available

68% VRAM used

Fit status

Runs well

Decode

58.0 tok/s

TTFT

3340 ms

Safe context

131K

Memory

96.0 GB / 141.0 GB

Memory breakdown

Weights75.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsPixtral Large 124B on NVIDIA H200 PCIe 141GB
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: 58.0 tok/s decode · 3.3s TTFT (warm) · 145 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	Runs well	58.0 tok/s	1822 ms	131K
Coding	S	Runs well	58.0 tok/s	3340 ms	131K
Agentic Coding	S	Runs well	58.0 tok/s	4858 ms	131K
Reasoning	S	Runs well	58.0 tok/s	3947 ms	131K
RAG	S	Runs well	58.0 tok/s	6072 ms	131K

Quantization options

How Pixtral Large 124B (124B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.4 GB	Low	A83
Q3_K_S	3	60.8 GB	Low	S85
NVFP4	4	69.4 GB	Medium	S86
Q4_K_M	4	75.6 GB	Medium	S87
Q5_K_M	5	89.3 GB	High	S87
Q6_KBest for your GPU	6	101.7 GB	High	S87
Q8_0	8	132.7 GB	Very High	F0
F16	16	254.2 GB	Maximum	F0

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 NVIDIA H200 PCIe 141GB See all hardware for Pixtral Large 124B

URL: https://willitrunai.com/can-run/pixtral-large-124b-on-h200-pcie-141gb

⇱ Pixtral Large 124B on NVIDIA H200 PCIe 141GB? YES