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URL: https://willitrunai.com/can-run/glm-4-9b-on-instinct-mi100-32gb

⇱ GLM-4 9B on AMD Instinct MI100 32GB? YES

Can GLM-4 9B run on AMD Instinct MI100 32GB?

YES — Runs Great

A70Great
○Estimated from fit model

GLM-4 9B needs ~10.2 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~126 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) — 10.2 GB, 126.0 tok/s, Runs well
10.2 GB required32.0 GB available
32% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

128K

Memory

10.2 GB / 32.0 GB

Memory breakdown

Weights5.5 GB
KV Cache0.6 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsGLM-4 9B 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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 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
ChatBRuns well126.0 tok/s838 ms128K
CodingARuns well126.0 tok/s1537 ms128K
Agentic CodingARuns well126.0 tok/s2235 ms128K
ReasoningARuns well126.0 tok/s1816 ms128K
RAGARuns well126.0 tok/s2794 ms128K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowB65
Q3_K_S
3
4.4 GB
LowB65
NVFP4
4
5.0 GB
MediumB65
Q4_K_M
4
5.5 GB
MediumB65
Q5_K_M
5
6.5 GB
HighB66
Q6_K
6
7.4 GB
HighB66
Q8_0
8
9.6 GB
Very HighB67
F16Best for your GPU
16
18.5 GB
MaximumA71

Get started

Copy-paste commands to run GLM-4 9B on your machine.

Run

ollama run glm4

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 GLM-4 9B