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

YES — Runs Great

C54Usable

Estimated from fit model

Llama 3.3 70B Instruct needs ~60.1 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) — 60.1 GB, 39.3 tok/s, Runs well

60.1 GB required80.0 GB available

75% VRAM used

Fit status

Runs well

Decode

39.3 tok/s

TTFT

4921 ms

Safe context

55K

Memory

60.1 GB / 80.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B Instruct on NVIDIA H100 PCIe 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: 39.3 tok/s decode · 4.9s TTFT (warm) · 98 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	C	Runs well	39.3 tok/s	2684 ms	55K
Coding	C	Runs well	39.3 tok/s	4921 ms	55K
Agentic Coding	C	Tight fit	39.3 tok/s	7157 ms	55K
Reasoning	C	Runs well	39.3 tok/s	5815 ms	55K
RAG	C	Tight fit	39.3 tok/s	8947 ms	55K

Quantization options

How Llama 3.3 70B Instruct (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	C44
Q3_K_S	3	34.3 GB	Low	C46
NVFP4	4	39.2 GB	Medium	C47
Q4_K_M	4	42.7 GB	Medium	C48
Q5_K_M	5	50.4 GB	High	C48
Q6_KBest for your GPU	6	57.4 GB	High	C48
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 Instruct on your machine.

Run

lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server start

Upgrade-Optionen

Hardware, die Llama 3.3 70B Instruct gut ausführt

NVIDIA H20 96GBGünstige Wahl

96 GB VRAM (+16)4000 GB/s (+2000)

Raises estimated decode speed by about 93%.75.9 tok/s Dekodierung

Raises estimated decode speed by about 93%.

ca. $12,000 MSRP

NVIDIA GH200 96GBBestes Preis-Leistungs-Verhältnis

96 GB VRAM (+16)4000 GB/s (+2000)

Raises estimated decode speed by about 93%.75.9 tok/s Dekodierung

Raises estimated decode speed by about 93%.

ca. $30,000 MSRP

Frequently asked questions

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

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

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 60.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B Instruct?

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

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

On NVIDIA H100 PCIe 80GB, Llama 3.3 70B Instruct 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 Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on NVIDIA H100 PCIe 80GB receives a C grade with 39.3 tok/s and 55K context.

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

On NVIDIA H100 PCIe 80GB, Llama 3.3 70B Instruct can safely use up to 55K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-3-70b-instruct-gguf-on-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: