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

YES — Runs Great

S95Excellent
Estimated from fit model

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

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 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 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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well58.0 tok/s1822 ms131K
CodingSRuns well58.0 tok/s3340 ms131K
Agentic CodingSRuns well58.0 tok/s4858 ms131K
ReasoningSRuns well58.0 tok/s3947 ms131K
RAGSRuns well58.0 tok/s6072 ms131K

Inference speed

Pixtral Large 124B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Pixtral Large 124B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~8 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
MacBook Pro M4 Max 128GB
128 GBQ4_K_M8.0Offloads
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M8.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M6.2Offloads
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M5.9Offloads
MacBook Pro M4 Max 64GB
64 GBQ4_K_M3.9Too big
2× RX 7900 XTX 24GB
48 GBQ4_K_M3.6Too big
NVIDIA2× RTX 4090 24GB
48 GBQ4_K_M2.6Too big
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M2.4Too big
NVIDIA2× RTX 3090 24GB
48 GBQ4_K_M2.2Too big
NVIDIARTX 5090 32GB
32 GBQ4_K_M2.0Too big
NVIDIARTX 4090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M2.0Too big
NVIDIARTX 3090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big
RX 7900 XTX 24GB
24 GBQ4_K_M2.0Too big
MacBook Pro M3 Max 64GB
64 GBQ4_K_M2.0Too big
MacBook Pro M1 Max 64GB
64 GBQ4_K_M2.0Too big
NVIDIA4× RTX 3060 12GB
48 GBQ4_K_M2.0Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
48.4 GB
LowA83
Q3_K_S
3
60.8 GB
LowS85
NVFP4
4
69.4 GB
MediumS86
Q4_K_M
4
75.6 GB
MediumS87
Q5_K_M
5
89.3 GB
HighS87
Q6_KBest for your GPU
6
101.7 GB
HighS87
Q8_0
8
132.7 GB
Very HighF0
F16
16
254.2 GB
MaximumF0

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 H200 141GB run Pixtral Large 124B?

Yes, NVIDIA H200 141GB can run Pixtral Large 124B with a S grade (Runs well). Expected decode speed: 58.0 tok/s.

How much VRAM does Pixtral Large 124B need?

Pixtral Large 124B (124B parameters) requires approximately 96.0 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 H200 141GB?

On NVIDIA H200 141GB, Pixtral Large 124B achieves approximately 58.0 tokens per second decode speed with a time-to-first-token of 3340ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Pixtral Large 124B for coding?

For coding workloads, Pixtral Large 124B on NVIDIA H200 141GB receives a S grade with 58.0 tok/s and 131K context.

What context window can Pixtral Large 124B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Pixtral Large 124B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H200 141GBSee all hardware for Pixtral Large 124B
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