Can glm 4 9b chat 1m run on RX 7900 XTX 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

glm 4 9b chat 1m needs ~9.8 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~126 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 9.8 GB, 125.9 tok/s, Runs well

9.8 GB required24.0 GB available

41% VRAM used

Fit status

Runs well

Decode

125.9 tok/s

TTFT

1538 ms

Safe context

231K

Memory

9.8 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m on RX 7900 XTX 24GB
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: 125.9 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	125.9 tok/s	839 ms	231K
Coding	C	Runs well	125.9 tok/s	1538 ms	231K
Agentic Coding	C	Runs well	125.9 tok/s	2237 ms	231K
Reasoning	C	Runs well	125.9 tok/s	1817 ms	231K
RAG	C	Runs well	125.9 tok/s	2796 ms	231K

Quantization options

How glm 4 9b chat 1m (9B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C45
Q3_K_S	3	4.4 GB	Low	C45
NVFP4	4	5.0 GB	Medium	C45
Q4_K_M	4	5.5 GB	Medium	C46
Q5_K_M	5	6.5 GB	High	C46
Q6_K	6	7.4 GB	High	C47
Q8_0	8	9.6 GB	Very High	C48
F16Best for your GPU	16	18.5 GB	Maximum	C49

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

Frequently asked questions

Can RX 7900 XTX 24GB run glm 4 9b chat 1m?

Yes, RX 7900 XTX 24GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 125.9 tok/s.

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

glm 4 9b chat 1m (9B parameters) requires approximately 9.8 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 7900 XTX 24GB?

On RX 7900 XTX 24GB, glm 4 9b chat 1m achieves approximately 125.9 tokens per second decode speed with a time-to-first-token of 1538ms using Q4_K_M quantization.

Can RX 7900 XTX 24GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on RX 7900 XTX 24GB receives a C grade with 125.9 tok/s and 231K context.

What context window can glm 4 9b chat 1m use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, glm 4 9b chat 1m can safely use up to 231K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XTX 24GB 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-7900-xtx-24gb" 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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