Can Pixtral Large 124B run on NVIDIA H100 PCIe 80GB?

BARELY — Tight on Memory

A78Great

Estimated from fit model

Pixtral Large 124B needs ~89.9 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~15 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 89.9 GB, 14.6 tok/s, Very compromised (needs ~8.3 GB host RAM)

89.9 GB required80.0 GB available

112% VRAM needed

9.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~8.3 GB host RAM)

Decode

14.6 tok/s

TTFT

13301 ms

Safe context

Memory

89.9 GB / 80.0 GB

Offload

10%

Memory breakdown

Weights75.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsPixtral Large 124B on NVIDIA H100 PCIe 80GB
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: 14.6 tok/s decode · 13.3s TTFT (warm) · 36 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 8.3 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	A	Very compromised (needs ~6.3 GB host RAM)	15.5 tok/s	6820 ms	4K
Coding	A	Very compromised (needs ~8.3 GB host RAM)	14.6 tok/s	13301 ms	4K
Agentic Coding	A	Very compromised (needs ~12.1 GB host RAM)	12.9 tok/s	21779 ms	4K
Reasoning	A	Very compromised (needs ~8.3 GB host RAM)	14.6 tok/s	15720 ms	4K
RAG	A	Very compromised (needs ~12.1 GB host RAM)	12.9 tok/s	27223 ms	4K

Quantization options

How Pixtral Large 124B (124B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.4 GB	Low	S87
Q3_K_SBest for your GPU	3	60.8 GB	Low	S87
NVFP4	4	69.4 GB	Medium	F0
Q4_K_M	4	75.6 GB	Medium	F0
Q5_K_M	5	89.3 GB	High	F0
Q6_K	6	101.7 GB	High	F0
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

Can NVIDIA H100 PCIe 80GB run Pixtral Large 124B?

Yes, NVIDIA H100 PCIe 80GB can run Pixtral Large 124B with a A grade (Very compromised (needs ~8.3 GB host RAM)). Expected decode speed: 14.6 tok/s.

How much VRAM does Pixtral Large 124B need?

Pixtral Large 124B (124B parameters) requires approximately 89.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Pixtral Large 124B?

The recommended quantization for Pixtral Large 124B is Q4_K_M, which balances quality and memory efficiency.

What speed will Pixtral Large 124B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Pixtral Large 124B achieves approximately 14.6 tokens per second decode speed with a time-to-first-token of 13301ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Pixtral Large 124B for coding?

For coding workloads, Pixtral Large 124B on NVIDIA H100 PCIe 80GB receives a A grade with 14.6 tok/s and 4K context.

What context window can Pixtral Large 124B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Pixtral Large 124B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Pixtral Large 124B feels slow on NVIDIA H100 PCIe 80GB?

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 NVIDIA H100 PCIe 80GB See all hardware for Pixtral Large 124B

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