Can glm 4 9b chat 1m run on RX 6700 XT 12GB?

YES — Runs Great

C54Usable

Estimated from fit model

glm 4 9b chat 1m needs ~8.6 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~36 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) — 8.6 GB, 36.4 tok/s, Runs well

8.6 GB required12.0 GB available

72% VRAM used

Fit status

Runs well

Decode

36.4 tok/s

TTFT

5323 ms

Safe context

67K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m 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: 36.4 tok/s decode · 5.3s TTFT (warm) · 91 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	C	Runs well	36.4 tok/s	2903 ms	67K
Coding	C	Runs well	36.4 tok/s	5323 ms	67K
Agentic Coding	C	Runs well	36.4 tok/s	7742 ms	67K
Reasoning	C	Runs well	36.4 tok/s	6291 ms	67K
RAG	C	Runs well	36.4 tok/s	9678 ms	67K

Quantization options

How glm 4 9b chat 1m (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	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run glm 4 9b chat 1m on your machine.

Run

lms load hf-bartowski--glm-4-9b-chat-1m-gguf && lms server start

Upgrade options

Hardware that runs glm 4 9b chat 1m well

RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+4)896 GB/s (+512)

Raises estimated decode speed by about 187%.104.5 tok/s decode

Raises estimated decode speed by about 187%.

Adds memory headroom for longer context windows and future model growth.

~$749 MSRP

RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+4)672 GB/s (+288)

Raises estimated decode speed by about 148%.90.1 tok/s decode

Raises estimated decode speed by about 148%.

Adds memory headroom for longer context windows and future model growth.

~$799 MSRP

Frequently asked questions

Can RX 6700 XT 12GB run glm 4 9b chat 1m?

Yes, RX 6700 XT 12GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 36.4 tok/s.

How much VRAM does glm 4 9b chat 1m need?

glm 4 9b chat 1m (9B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for glm 4 9b chat 1m?

The recommended quantization for glm 4 9b chat 1m is Q4_K_M, which balances quality and memory efficiency.

What speed will glm 4 9b chat 1m run at on RX 6700 XT 12GB?

On RX 6700 XT 12GB, glm 4 9b chat 1m achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5323ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on RX 6700 XT 12GB receives a C grade with 36.4 tok/s and 67K context.

What context window can glm 4 9b chat 1m use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, glm 4 9b chat 1m can safely use up to 67K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GB See all hardware for glm 4 9b chat 1m

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<iframe src="https://willitrunai.com/embed/hf-bartowski--glm-4-9b-chat-1m-gguf-on-rx-6700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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