Can Llama 3.1 70B run on NVIDIA H20 96GB?

YES — Runs Great

S86Excellent

○Estimated from fit model

Llama 3.1 70B needs ~58.4 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~83 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) — 58.4 GB, 82.5 tok/s, Runs well

58.4 GB required96.0 GB available

61% VRAM used

Fit status

Runs well

Decode

82.5 tok/s

TTFT

2346 ms

Safe context

128K

Memory

58.4 GB / 96.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsLlama 3.1 70B on NVIDIA H20 96GB
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: 82.5 tok/s decode · 2.3s TTFT (warm) · 206 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	82.5 tok/s	1280 ms	128K
Coding	S	Runs well	82.5 tok/s	2346 ms	128K
Agentic Coding	S	Runs well	82.5 tok/s	3413 ms	128K
Reasoning	S	Runs well	82.5 tok/s	2773 ms	128K
RAG	S	Runs well	82.5 tok/s	4266 ms	128K

Quantization options

How Llama 3.1 70B (70B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A73
Q3_K_S	3	34.3 GB	Low	A75
NVFP4	4	39.2 GB	Medium	A76
Q4_K_M	4	42.7 GB	Medium	A77
Q5_K_M	5	50.4 GB	High	A78
Q6_K	6	57.4 GB	High	A79
Q8_0Best for your GPU	8	74.9 GB	Very High	A79
F16	16	143.5 GB	Maximum	F0

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 H20 96GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	47 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	130.3 tok/s
👁 Mistral Mistral Small 4 119B	119B	S	141.2 tok/s
👁 OpenAI GPT-OSS 120B	117B	S	49.4 tok/s
👁 Cohere Command A 111B	111B	S	52.2 tok/s

Frequently asked questions

See all results for NVIDIA H20 96GB See all hardware for Llama 3.1 70B

URL: https://willitrunai.com/can-run/llama-3.1-70b-on-h20-96gb