Can Llama 3.1 70B run on NVIDIA A800 80GB?

YES — Runs Great

A85Great

○Estimated from fit model

Llama 3.1 70B needs ~56.8 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~35 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) — 56.8 GB, 38.4 tok/s, Runs well

56.8 GB required80.0 GB available

71% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5036 ms

Safe context

92K

Memory

56.8 GB / 80.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsLlama 3.1 70B on NVIDIA A800 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 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	A	Runs well	38.4 tok/s	2747 ms	92K
Coding	A	Runs well	35.3 tok/s	5477 ms	92K
Agentic Coding	S	Runs well	38.4 tok/s	7326 ms	92K
Reasoning	S	Runs well	38.4 tok/s	5952 ms	92K
RAG	S	Runs well	38.4 tok/s	9157 ms	92K

Quantization options

How Llama 3.1 70B (70B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A75
Q3_K_S	3	34.3 GB	Low	A77
NVFP4	4

Get started

Copy-paste commands to run Llama 3.1 70B on your machine.

Run

ollama run llama3.1

Your hardware

More models your NVIDIA A800 80GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	A	15.5 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	A

Frequently asked questions

See all results for NVIDIA A800 80GB See all hardware for Llama 3.1 70B

URL: https://willitrunai.com/can-run/llama-3.1-70b-on-a800-80gb

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