Can GLM-4 9B run on Intel Arc A770 16GB?

YES — Runs Great

A73Great

○Estimated from fit model

GLM-4 9B needs ~8.6 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.6 GB, 50.2 tok/s, Runs well

8.6 GB required16.0 GB available

54% VRAM used

Fit status

Runs well

Decode

50.2 tok/s

TTFT

3856 ms

Safe context

128K

Memory

8.6 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGLM-4 9B on Intel Arc A770 16GB
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: 50.2 tok/s decode · 3.9s TTFT (warm) · 126 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	50.2 tok/s	2103 ms	128K
Coding	A	Runs well	50.2 tok/s	3856 ms	128K
Agentic Coding	A	Runs well	50.2 tok/s	5609 ms	128K
Reasoning	A	Runs well	50.2 tok/s	4557 ms	128K
RAG	A	Runs well	50.2 tok/s	7011 ms	128K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B69
Q3_K_S	3	4.4 GB	Low	B70
NVFP4	4	5.0 GB	Medium	A70
Q4_K_M	4	5.5 GB	Medium	A71
Q5_K_M	5	6.5 GB	High	A72
Q6_K	6	7.4 GB	High	A73
Q8_0Best for your GPU	8	9.6 GB	Very High	A73
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

ollama run glm4

Your hardware

More models your Intel Arc A770 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	S	31.9 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	30.2 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	29.2 tok/s
👁 Mistral Ministral 3 14B	14B	S	31.7 tok/s
👁 Mistral Codestral 2 25.08	22B	A	10.7 tok/s

Frequently asked questions

See all results for Intel Arc A770 16GB See all hardware for GLM-4 9B

URL: https://willitrunai.com/can-run/glm-4-9b-on-arc-a770-16gb