Can Mixtral 8x22B run on NVIDIA L20 48GB?

NO — Won't Fit

F0Won't run

Estimated — low-sample bucket· few comparable runs

Mixtral 8x22B needs ~95.1 GB but NVIDIA L20 48GB only has 48.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity

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) — 95.1 GB, exceeds 48.0 GB available

95.1 GB required48.0 GB available

198% VRAM needed

47.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.4 tok/s

TTFT

79423 ms

Safe context

Memory

95.1 GB / 48.0 GB

Offload

50%

Memory breakdown

Weights86.0 GB

KV Cache3.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMixtral 8x22B on NVIDIA L20 48GB
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: 2.4 tok/s decode · 79.4s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 95.1 GB, but this setup only exposes 48.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.5 tok/s	41700 ms	4K
Coding	F	Too heavy	2.4 tok/s	79423 ms	4K
Agentic Coding	F	Too heavy	2.3 tok/s	124435 ms	4K
Reasoning	F	Too heavy	2.4 tok/s	93863 ms	4K
RAG	F	Too heavy	2.3 tok/s	155544 ms	4K

Quantization options

How Mixtral 8x22B (141B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	55.0 GB	Low	F0
Q3_K_S	3	69.1 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Mixtral 8x22B well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+48)1792 GB/s (+928)

Makes the model fit on the accelerator instead of staying completely out of reach.25.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+48)1597 GB/s (+733)

Makes the model fit on the accelerator instead of staying completely out of reach.22.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+48)4000 GB/s (+3136)

Makes the model fit on the accelerator instead of staying completely out of reach.62.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$12,000 MSRP

Frequently asked questions

Can NVIDIA L20 48GB run Mixtral 8x22B?

No, Mixtral 8x22B requires more memory than NVIDIA L20 48GB provides.

How much VRAM does Mixtral 8x22B need?

Mixtral 8x22B (141B parameters) requires approximately 95.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Mixtral 8x22B?

The recommended quantization for Mixtral 8x22B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mixtral 8x22B run at on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Mixtral 8x22B achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 79423ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Mixtral 8x22B for coding?

For coding workloads, Mixtral 8x22B on NVIDIA L20 48GB receives a F grade with 2.4 tok/s and 4K context.

What context window can Mixtral 8x22B use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Mixtral 8x22B can safely use up to 4K tokens of context. The model's official context limit is 66K, but available memory constrains the safe maximum.

What should I upgrade first if Mixtral 8x22B feels slow on NVIDIA L20 48GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for NVIDIA L20 48GB See all hardware for Mixtral 8x22B

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