Can Vicuna 13B run on AMD Instinct MI100 32GB?

YES — Runs Great

A78Great

Estimated from fit model

Vicuna 13B needs ~24.2 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~101 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 24.2 GB, 100.7 tok/s, Runs well

24.2 GB required32.0 GB available

76% VRAM used

Fit status

Runs well

Decode

100.7 tok/s

TTFT

1923 ms

Safe context

Memory

24.2 GB / 32.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsVicuna 13B on AMD Instinct MI100 32GB
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: 100.7 tok/s decode · 1.9s TTFT (warm) · 252 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	100.7 tok/s	1049 ms	4K
Coding	A	Runs well	100.7 tok/s	1923 ms	4K
Agentic Coding	B	Very compromised (needs ~1 GB host RAM)	57.4 tok/s	4905 ms	4K
Reasoning	A	Runs well	100.7 tok/s	2273 ms	4K
RAG	B	Very compromised (needs ~1 GB host RAM)	57.4 tok/s	6131 ms	4K

Quantization options

How Vicuna 13B (13B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B64
Q3_K_S	3	6.4 GB	Low	B65
NVFP4	4

Get started

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

Run

ollama run vicuna:13b

Your hardware

More models your AMD Instinct MI100 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	120.7 tok/s
Qwen 3.5 27B	27B	S	52.3 tok/s

Frequently asked questions

Can AMD Instinct MI100 32GB run Vicuna 13B?

Yes, AMD Instinct MI100 32GB can run Vicuna 13B with a A grade (Runs well). Expected decode speed: 100.7 tok/s.

How much VRAM does Vicuna 13B need?

Vicuna 13B (13B parameters) requires approximately 24.2 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 AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Vicuna 13B achieves approximately 100.7 tokens per second decode speed with a time-to-first-token of 1923ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run Vicuna 13B for coding?

For coding workloads, Vicuna 13B on AMD Instinct MI100 32GB receives a A grade with 100.7 tok/s and 4K context.

What context window can Vicuna 13B use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, 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 AMD Instinct MI100 32GB See all hardware for Vicuna 13B

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