Can Vicuna 13B run on NVIDIA A16 64GB?

YES — Runs Great

A70Great

Estimated from fit model

Vicuna 13B needs ~27.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 27.7 GB, 59.0 tok/s, Runs well

27.7 GB required64.0 GB available

43% VRAM used

Fit status

Runs well

Decode

59.0 tok/s

TTFT

3280 ms

Safe context

Memory

27.7 GB / 64.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsVicuna 13B on NVIDIA A16 64GB
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: 59.0 tok/s decode · 3.3s TTFT (warm) · 148 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	59.0 tok/s	1789 ms	4K
Coding	A	Runs well	59.0 tok/s	3280 ms	4K
Agentic Coding	A	Runs well	59.0 tok/s	4772 ms	4K
Reasoning	A	Runs well	59.0 tok/s	3877 ms	4K
RAG	A	Runs well	59.0 tok/s	5964 ms	4K

Quantization options

How Vicuna 13B (13B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B61
Q3_K_S	3	6.4 GB	Low	B61
NVFP4	4	7.3 GB	Medium	B61
Q4_K_M	4	7.9 GB	Medium	B62
Q5_K_M	5	9.4 GB	High	B62
Q6_K	6	10.7 GB	High	B62
Q8_0	8	13.9 GB	Very High	B63
F16Best for your GPU	16	26.7 GB	Maximum	B66

Get started

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

Run

ollama run vicuna:13b

Your hardware

More models your NVIDIA A16 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.5 27B	27B	S	30.7 tok/s
Qwen 3.6 27B	27B	S	30.8 tok/s
Qwen 3.6 35B A3B	35B	S	59.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	73.2 tok/s

Frequently asked questions

Can NVIDIA A16 64GB run Vicuna 13B?

Yes, NVIDIA A16 64GB can run Vicuna 13B with a A grade (Runs well). Expected decode speed: 59.0 tok/s.

How much VRAM does Vicuna 13B need?

Vicuna 13B (13B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Vicuna 13B?

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

What speed will Vicuna 13B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Vicuna 13B achieves approximately 59.0 tokens per second decode speed with a time-to-first-token of 3280ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Vicuna 13B for coding?

For coding workloads, Vicuna 13B on NVIDIA A16 64GB receives a A grade with 59.0 tok/s and 4K context.

What context window can Vicuna 13B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Vicuna 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 A16 64GB See all hardware for Vicuna 13B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/vicuna-13b-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: