Can Qwen 3 1.7B run on NVIDIA A100 80GB?

YES — Runs Great

B60Good

Estimated from fit model

Qwen 3 1.7B needs ~11.9 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~24 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) — 11.9 GB, 23.8 tok/s, Runs well

11.9 GB required80.0 GB available

15% VRAM used

Fit status

Runs well

Decode

23.8 tok/s

TTFT

8134 ms

Safe context

33K

Memory

11.9 GB / 80.0 GB

Memory breakdown

Weights1.0 GB

KV Cache1.7 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 3 1.7B on NVIDIA A100 80GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 23.8 tok/s decode · 8.1s TTFT (warm) · 60 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	B	Runs well	23.8 tok/s	4437 ms	33K
Coding	B	Runs well	23.8 tok/s	8134 ms	33K
Agentic Coding	B	Runs well	23.8 tok/s	11832 ms	33K
Reasoning	B	Runs well	23.8 tok/s	9613 ms	33K
RAG	B	Runs well	23.8 tok/s	14790 ms	33K

Inference speed

Qwen 3 1.7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3 1.7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~32 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?
RTX 5090 32GB	32 GB	Q4_K_M	32.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	27.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	23.8

Quantization options

How Qwen 3 1.7B (1.7000000476837158B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.7 GB	Low	B59
Q3_K_S	3	0.8 GB	Low	B59
NVFP4	4

Get started

Copy-paste commands to run Qwen 3 1.7B on your machine.

Run

ollama run qwen3:1.7b

Upgrade options

Hardware that runs Qwen 3 1.7B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.23.8 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.23.8 tok/s decode

~$3,999 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.23.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 3 1.7B?

Yes, NVIDIA A100 80GB can run Qwen 3 1.7B with a B grade (Runs well). Expected decode speed: 23.8 tok/s.

How much VRAM does Qwen 3 1.7B need?

Qwen 3 1.7B (1.7000000476837158B parameters) requires approximately 11.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 1.7B?

The recommended quantization for Qwen 3 1.7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 1.7B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3 1.7B achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8134ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 3 1.7B for coding?

For coding workloads, Qwen 3 1.7B on NVIDIA A100 80GB receives a B grade with 23.8 tok/s and 33K context.

What context window can Qwen 3 1.7B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3 1.7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Qwen 3 1.7B

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