Can Mixtral 8x22B run on RTX 3080 12GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Mixtral 8x22B needs ~91.5 GB but RTX 3080 12GB only has 12.0 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) — 91.5 GB, exceeds 12.0 GB available

91.5 GB required12.0 GB available

763% VRAM needed

79.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.3 tok/s

TTFT

85628 ms

Safe context

Memory

91.5 GB / 12.0 GB

Offload

90%

Memory breakdown

Weights86.0 GB

KV Cache3.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMixtral 8x22B on RTX 3080 12GB
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: 2.3 tok/s decode · 85.6s 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 91.5 GB, but this setup only exposes 12.0 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	2.3 tok/s	46706 ms	4K
Coding	F	Too heavy	2.3 tok/s	85628 ms	4K
Agentic Coding	F	Too heavy	2.3 tok/s	124551 ms	4K
Reasoning	F	Too heavy	2.3 tok/s	101197 ms	4K
RAG	F	Too heavy	2.3 tok/s	155688 ms	4K

Inference speed

Mixtral 8x22B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mixtral 8x22B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~14 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?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.5	Fits

Quantization options

How Mixtral 8x22B (141B params) fits at each quantization level on RTX 3080 12GB (12.0 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 (+84)1792 GB/s (+880)

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 (+84)1597 GB/s (+685)

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 H200 141GBBiggest leap

141 GB VRAM (+129)4800 GB/s (+3888)

Makes the model fit on the accelerator instead of staying completely out of reach.97.4 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.

~$30,000 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+116)

Makes the model fit on the accelerator instead of staying completely out of reach.4 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.

Frequently asked questions

Can RTX 3080 12GB run Mixtral 8x22B?

No, Mixtral 8x22B requires more memory than RTX 3080 12GB provides.

How much VRAM does Mixtral 8x22B need?

Mixtral 8x22B (141B parameters) requires approximately 91.5 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 RTX 3080 12GB?

On RTX 3080 12GB, Mixtral 8x22B achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 85628ms using Q4_K_M quantization.

Can RTX 3080 12GB run Mixtral 8x22B for coding?

For coding workloads, Mixtral 8x22B on RTX 3080 12GB receives a F grade with 2.3 tok/s and 4K context.

What context window can Mixtral 8x22B use on RTX 3080 12GB?

On RTX 3080 12GB, 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 RTX 3080 12GB?

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 RTX 3080 12GB See all hardware for Mixtral 8x22B

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