Can Llama 3.2 1B Instruct Q8 0 run on NVIDIA A100 80GB?

YES — Runs Great

D39Poor

Estimated from fit model

Llama 3.2 1B Instruct Q8 0 needs ~10.1 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q6_K quantization, expect ~14 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

Q6_K (High quality) — 10.1 GB, 14.0 tok/s, Runs well

10.1 GB required80.0 GB available

13% VRAM used

Fit status

Runs well

Decode

14.0 tok/s

TTFT

13829 ms

Safe context

9.6M

Memory

10.1 GB / 80.0 GB

Memory breakdown

Weights0.8 GB

KV Cache0.1 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsLlama 3.2 1B Instruct Q8 0 on NVIDIA A100 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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 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	D	Runs well	14.0 tok/s	7543 ms	5.6M
Coding	D	Runs well	14.0 tok/s	13829 ms	9.6M
Agentic Coding	D	Runs well	14.0 tok/s	20114 ms	9.6M
Reasoning	D	Runs well	14.0 tok/s	16343 ms	9.6M
RAG	D	Runs well	14.0 tok/s	25143 ms	9.6M

Quantization options

How Llama 3.2 1B Instruct Q8 0 (1B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	D40
Q3_K_S	3	0.5 GB	Low	D40
NVFP4	4	0.6 GB	Medium	D40
Q4_K_M	4	0.6 GB	Medium	D40
Q5_K_M	5	0.7 GB	High	D40
Q6_K	6	0.8 GB	High	D40
Q8_0	8	1.1 GB	Very High	D40
F16Best for your GPU	16	2.1 GB	Maximum	D40

Get started

Copy-paste commands to run Llama 3.2 1B Instruct Q8 0 on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF" \
  --hf-file "Llama-3.2-1B-Instruct-Q8_0-GGUF-Q6_K.gguf" \
  -c 4096 -ngl 99

アップグレードオプション

Llama 3.2 1B Instruct Q8 0を快適に動かすハードウェア

MacBook Pro M3 Max 128GBコスパ重視

128 GB Unified (+48)

This setup is broadly balanced for this model.14 tok/sデコード

〜$2,499 MSRP

Mac Studio M2 Ultra 128GBコスパ最良

128 GB Unified (+48)

This setup is broadly balanced for this model.14 tok/sデコード

〜$3,999 MSRP

NVIDIA DGX Spark 128GBNVIDIAアップグレード

128 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.14 tok/sデコード

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

Frequently asked questions

Can NVIDIA A100 80GB run Llama 3.2 1B Instruct Q8 0?

Yes, NVIDIA A100 80GB can run Llama 3.2 1B Instruct Q8 0 with a D grade (Runs well). Expected decode speed: 14.0 tok/s.

How much VRAM does Llama 3.2 1B Instruct Q8 0 need?

Llama 3.2 1B Instruct Q8 0 (1B parameters) requires approximately 10.1 GB of memory with Q6_K quantization.

What is the best quantization for Llama 3.2 1B Instruct Q8 0?

The recommended quantization for Llama 3.2 1B Instruct Q8 0 is Q6_K, which balances quality and memory efficiency.

What speed will Llama 3.2 1B Instruct Q8 0 run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Llama 3.2 1B Instruct Q8 0 achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13829ms using Q6_K quantization.

Can NVIDIA A100 80GB run Llama 3.2 1B Instruct Q8 0 for coding?

For coding workloads, Llama 3.2 1B Instruct Q8 0 on NVIDIA A100 80GB receives a D grade with 14.0 tok/s and 9.6M context.

What context window can Llama 3.2 1B Instruct Q8 0 use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Llama 3.2 1B Instruct Q8 0 can safely use up to 9.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Llama 3.2 1B Instruct Q8 0

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