Can GLM-4 9B run on RTX 2080 Ti 11GB?

YES — Runs Great

A78Great

Estimated from fit model

GLM-4 9B needs ~8.4 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 8.4 GB, 79.8 tok/s, Runs well

8.4 GB required11.0 GB available

76% VRAM used

Fit status

Runs well

Decode

79.8 tok/s

TTFT

2427 ms

Safe context

84K

Memory

8.4 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsGLM-4 9B on RTX 2080 Ti 11GB
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: 79.8 tok/s decode · 2.4s TTFT (warm) · 199 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	79.8 tok/s	1324 ms	84K
Coding	A	Runs well	79.8 tok/s	2427 ms	84K
Agentic Coding	A	Runs well	79.8 tok/s	3530 ms	84K
Reasoning	A	Runs well	79.8 tok/s	2868 ms	84K
RAG	A	Runs well	79.8 tok/s	4413 ms	84K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A72
Q3_K_S	3	4.4 GB	Low	A74
NVFP4	4

Get started

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

Run

ollama run glm4

Your hardware

More models your RTX 2080 Ti 11GB can run

Model	Params	Grade	Decode	Capabilities
OLMo 2 13B	13B	B	29.2 tok/s
Pixtral 12B	12B	B	35 tok/s

Frequently asked questions

Can RTX 2080 Ti 11GB run GLM-4 9B?

Yes, RTX 2080 Ti 11GB can run GLM-4 9B with a A grade (Runs well). Expected decode speed: 79.8 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 8.4 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 RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, GLM-4 9B achieves approximately 79.8 tokens per second decode speed with a time-to-first-token of 2427ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on RTX 2080 Ti 11GB receives a A grade with 79.8 tok/s and 84K context.

What context window can GLM-4 9B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, GLM-4 9B can safely use up to 84K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 2080 Ti 11GB See all hardware for GLM-4 9B

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