Can LLaVA 1.6 13B run on NVIDIA A100 80GB?

YES — Runs Great

A73Great

Estimated from fit model

LLaVA 1.6 13B needs ~29.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~182 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) — 29.3 GB, 182.0 tok/s, Runs well

29.3 GB required80.0 GB available

37% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

Memory

29.3 GB / 80.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsLLaVA 1.6 13B 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: 182.0 tok/s decode · 1.1s TTFT (warm) · 455 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	A	Runs well	182.0 tok/s	580 ms	4K
Coding	A	Runs well	182.0 tok/s	1064 ms	4K
Agentic Coding	A	Runs well	182.0 tok/s	1547 ms	4K
Reasoning	A	Runs well	182.0 tok/s	1257 ms	4K
RAG	A	Runs well	182.0 tok/s	1934 ms	4K

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B63
Q3_K_S	3	6.4 GB	Low	B63
NVFP4	4	7.3 GB	Medium	B63
Q4_K_M	4	7.9 GB	Medium	B63
Q5_K_M	5	9.4 GB	High	B63
Q6_K	6	10.7 GB	High	B64
Q8_0	8	13.9 GB	Very High	B64
F16Best for your GPU	16	26.7 GB	Maximum	B66

Get started

Copy-paste commands to run LLaVA 1.6 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "liuhaotian/llava-v1.6-mistral-7b" \
  --hf-file "llava-v1.6-mistral-7b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	259 tok/s
Qwen 3.5 27B	27B	S	112.3 tok/s
Qwen 3.6 27B	27B	S	112.7 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run LLaVA 1.6 13B?

Yes, NVIDIA A100 80GB can run LLaVA 1.6 13B with a A grade (Runs well). Expected decode speed: 182.0 tok/s.

How much VRAM does LLaVA 1.6 13B need?

LLaVA 1.6 13B (13B parameters) requires approximately 29.3 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.6 13B?

The recommended quantization for LLaVA 1.6 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.6 13B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, LLaVA 1.6 13B achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run LLaVA 1.6 13B for coding?

For coding workloads, LLaVA 1.6 13B on NVIDIA A100 80GB receives a A grade with 182.0 tok/s and 4K context.

What context window can LLaVA 1.6 13B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, LLaVA 1.6 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for LLaVA 1.6 13B

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