Can Llama 3.2 11B Vision run on NVIDIA H800 80GB?

YES — Runs Great

B61Good

Estimated from fit model

Llama 3.2 11B Vision needs ~17.9 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~154 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) — 17.9 GB, 154.0 tok/s, Runs well

17.9 GB required80.0 GB available

22% VRAM used

Fit status

Runs well

Decode

154.0 tok/s

TTFT

1257 ms

Safe context

16K

Memory

17.9 GB / 80.0 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision 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: 154.0 tok/s decode · 1.3s TTFT (warm) · 385 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	B	Runs well	154.0 tok/s	686 ms	16K
Coding	B	Runs well	154.0 tok/s	1257 ms	16K
Agentic Coding	B	Runs well	154.0 tok/s	1829 ms	16K
Reasoning	B	Runs well	154.0 tok/s	1486 ms	16K
RAG	B	Runs well	154.0 tok/s	2286 ms	16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	C54
Q3_K_S	3	5.4 GB	Low	C54
NVFP4	4

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Upgrade options

Hardware that runs Llama 3.2 11B Vision well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.74.3 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA H800 80GB run Llama 3.2 11B Vision?

Yes, NVIDIA H800 80GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 154.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 17.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 11B Vision run at on NVIDIA H800 80GB?

On NVIDIA H800 80GB, Llama 3.2 11B Vision achieves approximately 154.0 tokens per second decode speed with a time-to-first-token of 1257ms using Q4_K_M quantization.

Can NVIDIA H800 80GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on NVIDIA H800 80GB receives a B grade with 154.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on NVIDIA H800 80GB?

On NVIDIA H800 80GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for NVIDIA H800 80GB See all hardware for Llama 3.2 11B Vision

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