GLM 4 9B 0414 — Hardware Requirements & GPU Compatibility
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GLM 4 9B 0414 is a 9.4B-parameter open language model from Z.ai in the GLM 4 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 6.02 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 · 32K)Best on 24 GB (Q4_K_M · ~109 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~23 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GLM 4 9B 0414
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve zai-org/GLM-4-9B-0414 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~99 tok/s
- generation speed
- Cost per 1M output tokens
- $0.34
- per 1M output tokens
How Much VRAM Does GLM 4 9B 0414 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.4 GB | 5.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~150 tok/s | |
| 5.0 GB | 6.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~132 tok/s | |
| 6.0 GB | 7.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~109 tok/s | |
| 7.1 GB | 8.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~93 tok/s | |
| 8.1 GB | 9.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~81 tok/s | |
| 9.8 GB | 11 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~67 tok/s | |
| 19.2 GB | 20.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~34 tok/s |
Selected Q4_K_M
4-bit medium quantization6.6GBat 16K context
- Weights
- 5.6 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.4 GB free · ~109 tok/s
≥50 tok/s needs ≥463 GB/s · RTX 5090 ~194, RTX 3090 Ti ~109
Which Hardware Can Run GLM 4 9B 0414?
Q4_K_M · 6.0 GBGLM 4 9B 0414 (Q4_K_M) requires 6.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 33K context window can add up to 1.3 GB, bringing total usage to 7.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run GLM 4 9B 0414, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.4 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~82Simulate output on RTX 3080RTX 2080 Ti~67Simulate output on RTX 2080 TiRTX 3070 Ti~66Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~80Simulate output on RX 7900 XT32–40 GB
fits · 25.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~194Simulate output on RTX 5090M5 Max36 GB~54Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~22Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to GLM 4 9B 0414
GLM 4 family · 18 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| SenseNova Loop 9B Previewsensenova | — | 8.2 GB | ~103 | same size |
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| 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 |
Fine-tunes & variants(1)
Specifications
- Publisher
- Z.ai
- Family
- GLM 4
- Parameters
- 9.4B
- Architecture
- Glm4ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,552
- Release Date
- 2025-04-07
- License
- MIT
Frequently Asked Questions
How much VRAM does GLM 4 9B 0414 need?
GLM 4 9B 0414 requires 6.0 GB of VRAM at Q4_K_M, or 19.2 GB at BF16. Full 33K context adds up to 1.3 GB (7.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.4B × 4.8 bits ÷ 8 = 5.6 GB
KV Cache + Overhead ≈ 0.4 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.
KV Cache + Overhead ≈ 1.7 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for GLM 4 9B 0414?
For GLM 4 9B 0414, Q4_K_M (6.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.1 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 0414 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run GLM 4 9B 0414. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run GLM 4 9B 0414 locally?
Yes — GLM 4 9B 0414 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GLM 4 9B 0414?
At Q4_K_M, GLM 4 9B 0414 can reach ~797 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~109 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 ÷ 6.02 × 0.65 = ~864 tok/s
Estimated speed at Q4_K_M (6.0 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 0414?
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 4.00 GB.
Which GPUs can run GLM 4 9B 0414?
52 consumer GPUs can run GLM 4 9B 0414 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 0414?
151 devices with unified memory can run GLM 4 9B 0414 at Q4_K_M (6.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.