Can Mixtral 8x7B run on NVIDIA A2 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Mixtral 8x7B needs ~33.4 GB but NVIDIA A2 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: 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) — 33.4 GB, exceeds 16.0 GB available

33.4 GB required16.0 GB available

209% VRAM needed

17.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

33.4 GB / 16.0 GB

Offload

50%

Memory breakdown

Weights28.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMixtral 8x7B on NVIDIA A2 16GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 33.4 GB, but this setup only exposes 16.0 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	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Mixtral 8x7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mixtral 8x7B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~85 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	84.5	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	77.1	Fits

Quantization options

How Mixtral 8x7B (47B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.3 GB	Low	F0
Q3_K_S	3	23.0 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Mixtral 8x7B well

RTX 5090 32GBBest value

32 GB VRAM (+16)1792 GB/s (+1592)

Makes the model fit on the accelerator instead of staying completely out of reach.54.9 tok/s decode

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

Raises estimated decode speed by about 2645%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBNVIDIA upgrade

32 GB VRAM (+16)896 GB/s (+696)

Makes the model fit on the accelerator instead of staying completely out of reach.34.4 tok/s decode

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

Raises estimated decode speed by about 1620%.

~$2,499 MSRP

RTX A6000 48GBBudget pick

48 GB VRAM (+32)768 GB/s (+568)

Makes the model fit on the accelerator instead of staying completely out of reach.42 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.

~$4,650 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run Mixtral 8x7B?

No, Mixtral 8x7B requires more memory than NVIDIA A2 16GB provides.

How much VRAM does Mixtral 8x7B need?

Mixtral 8x7B (47B parameters) requires approximately 33.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Mixtral 8x7B?

The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mixtral 8x7B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Mixtral 8x7B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Mixtral 8x7B for coding?

For coding workloads, Mixtral 8x7B on NVIDIA A2 16GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Mixtral 8x7B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Mixtral 8x7B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Mixtral 8x7B feels slow on NVIDIA A2 16GB?

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 A2 16GB See all hardware for Mixtral 8x7B

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