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URL: https://willitrunai.com/can-run/hf-maziyarpanahi--llama-3-3-70b-instruct-gguf-on-h800-80gb

⇱ Llama 3.3 70B Instruct on NVIDIA H800 80GB? YES

Can Llama 3.3 70B Instruct run on NVIDIA H800 80GB?

YES — Runs Great

B55Good
○Estimated from fit model

Llama 3.3 70B Instruct needs ~60.1 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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, 56.9 tok/s, Runs well
60.1 GB required80.0 GB available
75% VRAM used

Fit status

Runs well

Decode

56.9 tok/s

TTFT

3402 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 H800 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: 56.9 tok/s decode · 3.4s TTFT (warm) · 142 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well56.9 tok/s1856 ms55K
CodingBRuns well56.9 tok/s3402 ms55K
Agentic CodingCTight fit56.9 tok/s4948 ms55K
ReasoningBRuns well56.9 tok/s4020 ms55K
RAGCTight fit56.9 tok/s6185 ms55K

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowC44
Q3_K_S
3
34.3 GB
LowC46
NVFP4
4
39.2 GB
MediumC47
Q4_K_M
4
42.7 GB
MediumC48
Q5_K_M
5
50.4 GB
HighC48
Q6_KBest for your GPU
6
57.4 GB
HighC48
Q8_0
8
74.9 GB
Very HighF0
F16
16
143.5 GB
MaximumF0

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

Frequently asked questions

See all results for NVIDIA H800 80GBSee all hardware for Llama 3.3 70B Instruct