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URL: https://willitrunai.com/can-run/pixtral-large-124b-on-h20-96gb

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Can Pixtral Large 124B run on NVIDIA H20 96GB?

YES — With Offload

S91Excellent
○Estimated from fit model

Pixtral Large 124B needs ~91.5 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Balanced
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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, 46.6 tok/s, Runs with offload
91.5 GB required96.0 GB available
95% VRAM used

Fit status

Runs with offload

Decode

46.6 tok/s

TTFT

4156 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 NVIDIA H20 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: 46.6 tok/s decode · 4.2s TTFT (warm) · 117 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

WorkloadGradeFitDecodeTTFTContext
ChatSTight fit46.6 tok/s2267 ms29K
CodingSRuns with offload42.8 tok/s4520 ms29K
Agentic CodingSRuns with offload (needs ~0.7 GB host RAM)39.1 tok/s7195 ms29K
ReasoningSRuns with offload46.6 tok/s4912 ms29K
RAGSRuns with offload (needs ~0.7 GB host RAM)39.1 tok/s8994 ms

Quantization options

How Pixtral Large 124B (124B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
48.4 GB
LowS87
Q3_K_S
3
60.8 GB
LowS87
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 NVIDIA H20 96GBSee all hardware for Pixtral Large 124B
29K
69.4 GB
Medium
S87
Q4_K_MBest for your GPU
4
75.6 GB
MediumS87
Q5_K_M
5
89.3 GB
HighF0
Q6_K
6
101.7 GB
HighF0
Q8_0
8
132.7 GB
Very HighF0
F16
16
254.2 GB
MaximumF0

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.