Can Llama 3.3 70B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Llama 3.3 70B needs ~56.8 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~39 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, 42.8 tok/s, Runs well

56.8 GB required80.0 GB available

71% VRAM used

Fit status

Runs well

Decode

42.8 tok/s

TTFT

4525 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 3.3 70B on NVIDIA H100 PCIe 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: 42.8 tok/s decode · 4.5s TTFT (warm) · 107 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	42.8 tok/s	2468 ms	92K
Coding	S	Runs well	39.3 tok/s	4921 ms	92K
Agentic Coding	S	Runs well	42.8 tok/s	6581 ms	92K
Reasoning	S	Runs well	42.8 tok/s	5347 ms	92K
RAG	S	Runs well	42.8 tok/s	8227 ms	92K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A78
Q3_K_S	3	34.3 GB	Low	A80
NVFP4	4

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 H100 PCIe 80GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen 3.5 122B A10B	122B	A

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Llama 3.3 70B?

Yes, NVIDIA H100 PCIe 80GB can run Llama 3.3 70B with a S grade (Runs well). Expected decode speed: 39.3 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 56.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Llama 3.3 70B achieves approximately 39.3 tokens per second decode speed with a time-to-first-token of 4921ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on NVIDIA H100 PCIe 80GB receives a S grade with 39.3 tok/s and 92K context.

What context window can Llama 3.3 70B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Llama 3.3 70B can safely use up to 92K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Llama 3.3 70B

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