Will It Run AI

Can Llama 3.3 70B Instruct run on NVIDIA GH200 96GB?

YES — Runs Great

B55Good
Estimated from fit model

Llama 3.3 70B Instruct needs ~61.7 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~76 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 61.7 GB, 75.9 tok/s, Runs well
61.7 GB required96.0 GB available
64% VRAM used

Fit status

Runs well

Decode

75.9 tok/s

TTFT

2551 ms

Safe context

83K

Memory

61.7 GB / 96.0 GB

Memory breakdown

Weights42.7 GB
KV Cache8.2 GB
Runtime1.2 GB
Headroom9.6 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B Instruct on NVIDIA GH200 96GB
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: 75.9 tok/s decode · 2.6s TTFT (warm) · 190 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
ChatCRuns well75.9 tok/s1392 ms83K
CodingBRuns well75.9 tok/s2551 ms83K
Agentic CodingBRuns well75.9 tok/s3711 ms83K
ReasoningBRuns well75.9 tok/s3015 ms83K
RAGBRuns well75.9 tok/s4639 ms83K

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowC42
Q3_K_S
3
34.3 GB
LowC44
NVFP4
4
39.2 GB
MediumC45
Q4_K_M
4
42.7 GB
MediumC46
Q5_K_M
5
50.4 GB
HighC47
Q6_K
6
57.4 GB
HighC48
Q8_0Best for your GPU
8
74.9 GB
Very HighC48
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

Can NVIDIA GH200 96GB run Llama 3.3 70B Instruct?

Yes, NVIDIA GH200 96GB can run Llama 3.3 70B Instruct with a B grade (Runs well). Expected decode speed: 75.9 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 61.7 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 GH200 96GB?

On NVIDIA GH200 96GB, Llama 3.3 70B Instruct achieves approximately 75.9 tokens per second decode speed with a time-to-first-token of 2551ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on NVIDIA GH200 96GB receives a B grade with 75.9 tok/s and 83K context.

What context window can Llama 3.3 70B Instruct use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Llama 3.3 70B Instruct can safely use up to 83K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA GH200 96GBSee all hardware for Llama 3.3 70B Instruct
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