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URL: https://willitrunai.com/can-run/vicuna-13b-on-instinct-mi100-32gb

⇱ Vicuna 13B on AMD Instinct MI100 32GB? YES

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
Share:

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

4K

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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well100.7 tok/s1049 ms4K
CodingARuns well100.7 tok/s1923 ms4K
Agentic CodingBVery compromised (needs ~1 GB host RAM)57.4 tok/s4905 ms4K
ReasoningARuns well100.7 tok/s2273 ms4K
RAGBVery compromised (needs ~1 GB host RAM)57.4 tok/s6131 ms4K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowB64
Q3_K_S
3
6.4 GB
LowB65
NVFP4
4
7.3 GB
MediumB65
Q4_K_M
4
7.9 GB
MediumB65
Q5_K_M
5
9.4 GB
HighB66
Q6_K
6
10.7 GB
HighB67
Q8_0
8
13.9 GB
Very HighB68
F16Best for your GPU
16
26.7 GB
MaximumB69

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

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct		30.5BS120.7 tok/s
👁 Alibaba
Qwen 3.5 27B		27BS52.3 tok/s
👁 Alibaba
Qwen 3.6 27B		27BS32.6 tok/s
👁 Alibaba
Qwen 3.6 35B A3B		35BS101.4 tok/s
👁 Alibaba
Qwen3-VL 30B A3B Instruct		30BS124.8 tok/s

Frequently asked questions

See all results for AMD Instinct MI100 32GBSee all hardware for Vicuna 13B