Can Llama 3.2 3B Instruct run on NVIDIA H100 80GB?

YES — Runs Great

C43Usable

Estimated from fit model

Llama 3.2 3B Instruct needs ~11.7 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q5_K_M quantization, expect ~42 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

Q5_K_M (High quality) — 11.7 GB, 42.0 tok/s, Runs well

11.7 GB required80.0 GB available

15% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

3.1M

Memory

11.7 GB / 80.0 GB

Memory breakdown

Weights2.2 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsLlama 3.2 3B Instruct on NVIDIA H100 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	42.0 tok/s	2514 ms	3.1M
Coding	C	Runs well	42.0 tok/s	4610 ms	3.1M
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	3.1M
Reasoning	C	Runs well	42.0 tok/s	5448 ms	3.1M
RAG	C	Runs well	42.0 tok/s	8381 ms	3.1M

Inference speed

Llama 3.2 3B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.2 3B Instruct at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~57 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	57.0	Fits
RTX 4090 24GB	24 GB	Q5_K_M	48.0	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	48.0

Quantization options

How Llama 3.2 3B Instruct (3B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	D40
Q3_K_S	3	1.5 GB	Low	D40
NVFP4	4

Get started

Copy-paste commands to run Llama 3.2 3B Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bartowski/Llama-3.2-3B-Instruct-GGUF" \
  --hf-file "Llama-3.2-3B-Instruct-GGUF-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Llama 3.2 3B Instruct well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.42 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.42 tok/s decode

~$3,999 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.42 tok/s decode

Adds memory headroom for longer context windows and future model growth.

Frequently asked questions

Can NVIDIA H100 80GB run Llama 3.2 3B Instruct?

Yes, NVIDIA H100 80GB can run Llama 3.2 3B Instruct with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Llama 3.2 3B Instruct need?

Llama 3.2 3B Instruct (3B parameters) requires approximately 11.7 GB of memory with Q5_K_M quantization.

What is the best quantization for Llama 3.2 3B Instruct?

The recommended quantization for Llama 3.2 3B Instruct is Q5_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 3B Instruct run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Llama 3.2 3B Instruct achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q5_K_M quantization.

Can NVIDIA H100 80GB run Llama 3.2 3B Instruct for coding?

For coding workloads, Llama 3.2 3B Instruct on NVIDIA H100 80GB receives a C grade with 42.0 tok/s and 3.1M context.

What context window can Llama 3.2 3B Instruct use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Llama 3.2 3B Instruct can safely use up to 3.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Llama 3.2 3B Instruct

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