GLM-4-9B-Chat
zai-org/GLM-4-9B-Chat
Chinese-strong 9B chat model by Zhipu AI.
Parameters
9.4B
Context
128K
Downloads
590K
Likes
1.9K
Architecture
chatglm
GPU Requirements
VRAM / RAM / disk estimates by quantization and usage scenario
| Quantization | Minimum | Recommended | Production |
|---|---|---|---|
| FP1616bit | 27.1 GB RAM 40.7 · Disk 66.3 | 43.9 GB RAM 65.8 · Disk 92.3 | 141.8 GB RAM 212.6 · Disk 131.3 |
| FP88bit | 16.0 GB RAM 32.0 · Disk 52.7 | 32.3 GB RAM 48.4 · Disk 78.7 | 129.7 GB RAM 194.5 · Disk 117.7 |
| INT88bit | 16.0 GB RAM 32.0 · Disk 52.7 | 32.3 GB RAM 48.4 · Disk 78.7 | 129.7 GB RAM 194.5 · Disk 117.7 |
| AWQ4bit | 10.7 GB RAM 32.0 · Disk 46.1 | 26.7 GB RAM 40.1 · Disk 72.1 | 123.9 GB RAM 185.8 · Disk 111.1 |
| GPTQ4bit | 10.8 GB RAM 32.0 · Disk 46.2 | 26.8 GB RAM 40.2 · Disk 72.2 | 123.9 GB RAM 185.9 · Disk 111.2 |
| GGUF4bit | 10.9 GB RAM 32.0 · Disk 46.3 | 26.9 GB RAM 40.4 · Disk 72.3 | 124.1 GB RAM 186.1 · Disk 111.3 |
GPU Recommendations
1× H100 SXM
80 GB VRAM
≈ 737.9 tok/s
Effective production throughput ≈ 516.5 tok/s
score 0.5786
2× Tesla V100
32 GB VRAM
≈ 0 tok/s (N/A — benchmark unavailable)
Effective production throughput ≈ 0 tok/s
score 0.6245
8× H200 SXM
1128 GB VRAM
≈ 3383.3 tok/s
Effective production throughput ≈ 2368.3 tok/s
score 0.2197
1× RTX A6000
48 GB VRAM
≈ 169.2 tok/s
Effective production throughput ≈ 118.4 tok/s
score 0.6395
💬 Community — real deployment experience
0 Articles · 0 Benchmarks
GPUs that fit (single card)
RTX A6000
48 GB VRAM
$210
/mo · vast
RTX 5090
32 GB VRAM
$244
/mo · vast
L40S
48 GB VRAM
$341
/mo · vast
RTX PRO 6000 WS
48 GB VRAM
$586
/mo · vast
A100 80GB
80 GB VRAM
$876
/mo · vast