Can Mixtral 8x22B run on NVIDIA A30 24GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Mixtral 8x22B needs ~92.7 GB but NVIDIA A30 24GB only has 24.0 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) — 92.7 GB, exceeds 24.0 GB available

92.7 GB required24.0 GB available

386% VRAM needed

68.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.6 tok/s

TTFT

73399 ms

Safe context

Memory

92.7 GB / 24.0 GB

Offload

70%

Memory breakdown

Weights86.0 GB

KV Cache3.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMixtral 8x22B on NVIDIA A30 24GB
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: 2.6 tok/s decode · 73.4s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 92.7 GB, but this setup only exposes 24.0 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	2.6 tok/s	40036 ms	4K
Coding	F	Too heavy	2.6 tok/s	73399 ms	4K
Agentic Coding	F	Too heavy	2.6 tok/s	106763 ms	4K
Reasoning	F	Too heavy	2.6 tok/s	86745 ms	4K
RAG	F	Too heavy	2.6 tok/s	133454 ms	4K

Quantization options

How Mixtral 8x22B (141B params) fits at each quantization level on NVIDIA A30 24GB (24.0 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

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+72)1792 GB/s (+859)

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

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+72)1597 GB/s (+664)

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
NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+72)4000 GB/s (+3067)

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

See all results for NVIDIA A30 24GB See all hardware for Mixtral 8x22B

URL: https://willitrunai.com/can-run/mixtral-8x22b-on-a30-24gb

⇱

Can Mixtral 8x22B run on NVIDIA A30 24GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Mixtral 8x22B well

Frequently asked questions