Can GLM-4 9B run on RX 6700 XT 12GB?

YES — Runs Great

A75Great

Estimated from fit model

GLM-4 9B needs ~8.2 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 8.2 GB, 39.8 tok/s, Runs well

8.2 GB required12.0 GB available

68% VRAM used

Fit status

Runs well

Decode

39.8 tok/s

TTFT

4867 ms

Safe context

116K

Memory

8.2 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGLM-4 9B on RX 6700 XT 12GB
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: 39.8 tok/s decode · 4.9s TTFT (warm) · 100 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	39.8 tok/s	2655 ms	116K
Coding	A	Runs well	39.8 tok/s	4867 ms	116K
Agentic Coding	A	Runs well	39.8 tok/s	7079 ms	116K
Reasoning	A	Runs well	39.8 tok/s	5751 ms	116K
RAG	A	Runs well	39.8 tok/s	8848 ms	116K

Inference speed

GLM-4 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GLM-4 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A71
Q3_K_S	3	4.4 GB	Low	A73
NVFP4	4

Get started

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

Run

ollama run glm4

Your hardware

More models your RX 6700 XT 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 14B	14B	A	15.8 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	12.8 tok/s

Frequently asked questions

Can RX 6700 XT 12GB run GLM-4 9B?

Yes, RX 6700 XT 12GB can run GLM-4 9B with a A grade (Runs well). Expected decode speed: 39.8 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.

What is the best quantization for GLM-4 9B?

The recommended quantization for GLM-4 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will GLM-4 9B run at on RX 6700 XT 12GB?

On RX 6700 XT 12GB, GLM-4 9B achieves approximately 39.8 tokens per second decode speed with a time-to-first-token of 4867ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on RX 6700 XT 12GB receives a A grade with 39.8 tok/s and 116K context.

What context window can GLM-4 9B use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, GLM-4 9B can safely use up to 116K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GB See all hardware for GLM-4 9B

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