Can Pixtral Large 124B run on B100 192GB?

YES — Runs Great

S93Excellent

Estimated from fit model

Pixtral Large 124B needs ~101.1 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~97 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) — 101.1 GB, 96.6 tok/s, Runs well

101.1 GB required192.0 GB available

53% VRAM used

Fit status

Runs well

Decode

96.6 tok/s

TTFT

2004 ms

Safe context

131K

Memory

101.1 GB / 192.0 GB

Memory breakdown

Weights75.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsPixtral Large 124B on B100 192GB
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: 96.6 tok/s decode · 2.0s TTFT (warm) · 242 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	96.6 tok/s	1093 ms	131K
Coding	S	Runs well	96.6 tok/s	2004 ms	131K
Agentic Coding	S	Runs well	96.6 tok/s	2915 ms	131K
Reasoning	S	Runs well	96.6 tok/s	2368 ms	131K
RAG	S	Runs well	96.6 tok/s	3643 ms	131K

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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	8.0	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	8.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	6.2	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	5.9	Offloads
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	3.9	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	3.6	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.6	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.4	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.2	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	Too 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 B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.4 GB	Low	A81
Q3_K_S	3	60.8 GB	Low	A82
NVFP4	4	69.4 GB	Medium	A83
Q4_K_M	4	75.6 GB	Medium	A84
Q5_K_M	5	89.3 GB	High	S85
Q6_K	6	101.7 GB	High	S87
Q8_0Best for your GPU	8	132.7 GB	Very High	S87
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

Your hardware

More models your B100 192GB can run

Model	Params	Grade	Decode	Capabilities
DeepSeek V4 Flash	284B	S	144.8 tok/s

Frequently asked questions

Can B100 192GB run Pixtral Large 124B?

Yes, B100 192GB can run Pixtral Large 124B with a S grade (Runs well). Expected decode speed: 96.6 tok/s.

How much VRAM does Pixtral Large 124B need?

Pixtral Large 124B (124B parameters) requires approximately 101.1 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 B100 192GB?

On B100 192GB, Pixtral Large 124B achieves approximately 96.6 tokens per second decode speed with a time-to-first-token of 2004ms using Q4_K_M quantization.

Can B100 192GB run Pixtral Large 124B for coding?

For coding workloads, Pixtral Large 124B on B100 192GB receives a S grade with 96.6 tok/s and 131K context.

What context window can Pixtral Large 124B use on B100 192GB?

On B100 192GB, 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 B100 192GB See all hardware for Pixtral Large 124B

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