Can GLM-4 9B run on RTX 5000 Ada 32GB?

YES — Runs Great

A70Great

Estimated from fit model

GLM-4 9B needs ~10.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~92 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) — 10.5 GB, 91.8 tok/s, Runs well

10.5 GB required32.0 GB available

33% VRAM used

Fit status

Runs well

Decode

91.8 tok/s

TTFT

2109 ms

Safe context

128K

Memory

10.5 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGLM-4 9B on RTX 5000 Ada 32GB
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: 91.8 tok/s decode · 2.1s TTFT (warm) · 230 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	91.8 tok/s	1150 ms	128K
Coding	A	Runs well	91.8 tok/s	2109 ms	128K
Agentic Coding	A	Runs well	91.8 tok/s	3067 ms	128K
Reasoning	A	Runs well	91.8 tok/s	2492 ms	128K
RAG	A	Runs well	91.8 tok/s	3834 ms	128K

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	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	121.7	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	111.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	92.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	87.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	75.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	74.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	74.7	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	47.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	38.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	36.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B65
Q3_K_S	3	4.4 GB	Low	B65
NVFP4	4	5.0 GB	Medium	B65
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_M	5	6.5 GB	High	B66
Q6_K	6	7.4 GB	High	B66
Q8_0	8	9.6 GB	Very High	B67
F16Best for your GPU	16	18.5 GB	Maximum	A71

Get started

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

Run

ollama run glm4

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
Qwen 3.5 27B	27B	S	30.2 tok/s
Qwen 3.6 27B	27B	S	30.3 tok/s
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.1 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run GLM-4 9B?

Yes, RTX 5000 Ada 32GB can run GLM-4 9B with a A grade (Runs well). Expected decode speed: 91.8 tok/s.

How much VRAM does GLM-4 9B need?

GLM-4 9B (9B parameters) requires approximately 10.5 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, GLM-4 9B achieves approximately 91.8 tokens per second decode speed with a time-to-first-token of 2109ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run GLM-4 9B for coding?

For coding workloads, GLM-4 9B on RTX 5000 Ada 32GB receives a A grade with 91.8 tok/s and 128K context.

What context window can GLM-4 9B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, GLM-4 9B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for GLM-4 9B

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