Can Qwen 3 32B run on NVIDIA A100 80GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Qwen 3 32B needs ~32.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 32.6 GB, 95.4 tok/s, Runs well

32.6 GB required80.0 GB available

41% VRAM used

Fit status

Runs well

Decode

95.4 tok/s

TTFT

2029 ms

Safe context

131K

Memory

32.6 GB / 80.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 3 32B on NVIDIA A100 80GB
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: 95.4 tok/s decode · 2.0s TTFT (warm) · 239 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	95.4 tok/s	1107 ms	131K
Coding	S	Runs well	95.4 tok/s	2029 ms	131K
Agentic Coding	S	Runs well	95.4 tok/s	2951 ms	131K
Reasoning	S	Runs well	95.4 tok/s	2398 ms	131K
RAG	S	Runs well	95.4 tok/s	3689 ms	131K

Quantization options

How Qwen 3 32B (32B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A82
Q3_K_S	3	15.7 GB	Low	A82
NVFP4	4	17.9 GB	Medium	A82
Q4_K_M	4	19.5 GB	Medium	A83
Q5_K_M	5	23.0 GB	High	A83
Q6_K	6	26.2 GB	High	A84
Q8_0	8	34.2 GB	Very High	S86
F16Best for your GPU	16	65.6 GB	Maximum	S89

Get started

Copy-paste commands to run Qwen 3 32B on your machine.

Run

ollama run qwen3:32b

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s
Qwen 3.6 35B A3B	35B	S	217.7 tok/s
Qwen 3.5 35B A3B	35B	S	236.7 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 3 32B?

Yes, NVIDIA A100 80GB can run Qwen 3 32B with a S grade (Runs well). Expected decode speed: 95.4 tok/s.

How much VRAM does Qwen 3 32B need?

Qwen 3 32B (32B parameters) requires approximately 32.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 32B?

The recommended quantization for Qwen 3 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 32B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3 32B achieves approximately 95.4 tokens per second decode speed with a time-to-first-token of 2029ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 3 32B for coding?

For coding workloads, Qwen 3 32B on NVIDIA A100 80GB receives a S grade with 95.4 tok/s and 131K context.

What context window can Qwen 3 32B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3 32B 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 NVIDIA A100 80GB See all hardware for Qwen 3 32B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3-32b-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

95.4 tok/s

1107 ms

131K

Coding

Runs well

95.4 tok/s

2029 ms

131K

Agentic Coding

Runs well

95.4 tok/s

2951 ms

131K

Reasoning

Runs well

95.4 tok/s

2398 ms

131K

RAG

Runs well

95.4 tok/s

3689 ms

131K

Quantization options

How Qwen 3 32B (32B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A82
Q3_K_S	3	15.7 GB	Low	A82
NVFP4	4	17.9 GB	Medium	A82
Q4_K_M	4	19.5 GB	Medium	A83
Q5_K_M	5	23.0 GB	High	A83
Q6_K	6	26.2 GB	High	A84
Q8_0	8	34.2 GB	Very High	S86
F16Best for your GPU	16	65.6 GB	Maximum	S89

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

17.6 tok/s

Qwen 3.5 122B A10B

122B

52.1 tok/s

Qwen 3.6 35B A3B

35B

217.7 tok/s

Qwen 3.5 35B A3B

35B

236.7 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 3 32B?

Yes, NVIDIA A100 80GB can run Qwen 3 32B with a S grade (Runs well). Expected decode speed: 95.4 tok/s.

How much VRAM does Qwen 3 32B need?

Qwen 3 32B (32B parameters) requires approximately 32.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 32B?

The recommended quantization for Qwen 3 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 32B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3 32B achieves approximately 95.4 tokens per second decode speed with a time-to-first-token of 2029ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 3 32B for coding?

For coding workloads, Qwen 3 32B on NVIDIA A100 80GB receives a S grade with 95.4 tok/s and 131K context.

What context window can Qwen 3 32B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3 32B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.