Can Llama 3.3 70B run on NVIDIA A16 64GB?

YES — Tight Fit

A82Great

○Estimated from fit model

Llama 3.3 70B needs ~55.2 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 55.2 GB, 11.9 tok/s, Tight fit

55.2 GB required64.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

11.9 tok/s

TTFT

16243 ms

Safe context

45K

Memory

55.2 GB / 64.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B on NVIDIA A16 64GB
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: 11.9 tok/s decode · 16.2s TTFT (warm) · 30 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	Tight fit	11.9 tok/s	8860 ms	45K
Coding	A	Tight fit	11.9 tok/s	16243 ms	45K
Agentic Coding	A	Tight fit	11.9 tok/s	23626 ms	45K
Reasoning	A	Tight fit	11.9 tok/s	19196 ms	45K
RAG	A	Tight fit	11.9 tok/s	29532 ms	45K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A80
Q3_K_S	3	34.3 GB	Low	A82
NVFP4	4	39.2 GB	Medium	A82
Q4_K_M	4	42.7 GB	Medium	A82
Q5_K_MBest for your GPU	5	50.4 GB	High	A82
Q6_K	6	57.4 GB	High	F0
Q8_0	8	74.9 GB	Very High	F0
F16	16	143.5 GB	Maximum	F0

Get started

Copy-paste commands to run Llama 3.3 70B on your machine.

Run

ollama run llama3.3

Your hardware

More models your NVIDIA A16 64GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 2.5 VL 72B	72B	S	11.6 tok/s
👁 Alibaba Qwen3-Coder-Next	80B	S	31.6 tok/s

Frequently asked questions

See all results for NVIDIA A16 64GB See all hardware for Llama 3.3 70B

URL: https://willitrunai.com/can-run/llama-3.3-70b-on-a16-64gb

⇱ Llama 3.3 70B on NVIDIA A16 64GB? TIGHT FIT