Glm 4 9B Chat — Hardware Requirements & GPU Compatibility
Chat
Glm 4 9B Chat is a 9.4B-parameter open language model from Tsinghua in the GLM 4 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 6.20 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~106 tok/s · 64K)Macs from 32 GB (~14 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Glm 4 9B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve THUDM/glm-4-9b-chat --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (3 GB headroom) · Q4_K_M
- Generation speed
- ~38 tok/s
- generation speed
- Cost per 1M output tokens
- $0.56
- per 1M output tokens
How Much VRAM Does Glm 4 9B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.4 GB | 10.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~149 tok/s | |
| 5.0 GB | 10.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~130 tok/s | |
| 6.2 GB | 12 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~106 tok/s | |
| 7.4 GB | 13.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~89 tok/s | |
| 8.5 GB | 14.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~77 tok/s | |
| 10.3 GB | 16.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~63 tok/s | |
| 20.7 GB | 26.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 16K context · ~32 tok/s |
Selected Q4_K_M
4-bit medium quantization8.9GBat 16K context
- Weights
- 5.5 GB
- KV cache
- 3.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 15.1 GB free · ~106 tok/s
≥50 tok/s needs ≥476 GB/s · RTX 5090 ~188, RTX 3090 Ti ~106
Which Hardware Can Run Glm 4 9B Chat?
Q4_K_M · 6.2 GBGlm 4 9B Chat (Q4_K_M) requires 6.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 159 devices with unified memory can run Glm 4 9B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin Nano 8GB (Super).
12 GB
12 GB tier →fits · 3.1 GB free · up to 32K ctx
10 GPUs · 1 device family
RTX 3080 Ti~96Simulate output on RTX 3080 TiRTX 5070~71Simulate output on RTX 5070RTX 4070~53Simulate output on RTX 4070iPhone~9Simulate output on Apple iPhone 17 Pro20 GB
fits · 11.1 GB free · up to 64K ctx
1 GPU
RX 7900 XT~77Simulate output on RX 7900 XT32–40 GB
fits · 23.1 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~188Simulate output on RTX 5090M5 Max36 GB~52Simulate output on Mac Studio M5 Max (36 GB)M532 GB~17Simulate output on MacBook Air 13" M5 (32 GB)Orin~22Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Glm 4 9B Chat
GLM 4 family · 18 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| ZDTaichu5.0 9BTaichuAI | — | 9.2 GB | ~101 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| ZAYA1 VL 8BZyphra | — | 7.5 GB | ~346 | same size |
| Granite 4.1 8BIBM | — | 8.3 GB | ~111 | same size |
| Qwopus3.5 9B v3Jackrong | — | 6.6 GB | ~103 | same size |
Specifications
- Publisher
- Tsinghua
- Family
- GLM 4
- Parameters
- 9.4B
- Architecture
- ChatGLMModel
- Context Length
- 131,072 tokens
- Release Date
- 2024-06-04
- License
- Other
Frequently Asked Questions
How much VRAM does Glm 4 9B Chat need?
Glm 4 9B Chat requires 6.2 GB of VRAM at Q4_K_M, or 20.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 9.4B × 4.8 bits ÷ 8 = 5.6 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Glm 4 9B Chat?
For Glm 4 9B Chat, Q4_K_M (6.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Glm 4 9B Chat on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Glm 4 9B Chat is Mac Mini M1 (8 GB) at Q2_K; 156 of the 156 Macs we list can run it at some quantization. For Q4_K_M (6.2 GB) you need a Mac with more unified memory.
Can I run Glm 4 9B Chat locally?
Yes — Glm 4 9B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Glm 4 9B Chat?
At Q4_K_M, Glm 4 9B Chat can reach ~774 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~106 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 6.2 × 0.65 = ~839 tok/s
Estimated speed at Q4_K_M (6.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Glm 4 9B Chat?
At Q4_K_M, the download is about 5.64 GB. The full-precision BF16 version is 18.80 GB. The smallest option (Q2_K) is 3.99 GB.
Which GPUs can run Glm 4 9B Chat?
52 consumer GPUs can run Glm 4 9B Chat at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Glm 4 9B Chat?
159 devices with unified memory can run Glm 4 9B Chat at Q4_K_M (6.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.