GLM-4-32B-0414 on NVIDIA A100 80GB SXM (80GB VRAM)
In 4-bit Q4_K_M, GLM-4-32B-0414 uses only 22.55 GB of your 80 GB VRAM (28% utilization) at ~65.2 tokens/sec. In full 16-bit precision, it requires 68.49 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
22.55 GB / 80 GB🚀 Ready-to-Run Launch Command
ollama run glm4:32b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf THUDM/GLM-4-32B-0414-GGUF -c 8192 -ngl 99 -fa
Need 22.55 GB+ VRAM to run GLM-4-32B-0414?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 68.49 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
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All Quantization Formats: GLM-4-32B-0414 on NVIDIA A100 80GB SXM
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA A100 80GB SXM (80GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 65.92 GB | 1.91 GB | 68.49 GB | Yes (86%) | ~19.8 tok/s | 1x NVIDIA A100 80GB SXM |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 35.02 GB | 1.91 GB | 37.59 GB | Yes (47%) | ~37.2 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 35.02 GB | 1.91 GB | 37.59 GB | Yes (47%) | ~37.2 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF Q6_K (6-bit) | 6.56 bpw | 27.03 GB | 1.91 GB | 29.59 GB | Yes (37%) | ~48.2 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 23.44 GB | 1.91 GB | 26.01 GB | Yes (33%) | ~55.5 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 19.98 GB | 1.91 GB | 22.55 GB | Yes (28%) | ~65.2 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 17.51 GB | 1.91 GB | 20.08 GB | Yes (25%) | ~74.4 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 16.11 GB | 1.91 GB | 18.68 GB | Yes (23%) | ~80.8 tok/s | 1x NVIDIA A100 80GB SXM |
Frequently Asked Questions (GLM-4-32B-0414 + NVIDIA A100 80GB SXM)
Can NVIDIA A100 80GB SXM (80GB) run GLM-4-32B-0414 locally?
Yes — NVIDIA A100 80GB SXM (80GB) easily runs GLM-4-32B-0414 even in full FP16. In 4-bit Q4_K_M, GLM-4-32B-0414 uses only 22.55 GB of your 80 GB VRAM (28% utilization) at ~65.2 tokens/sec. In full 16-bit precision, it requires 68.49 GB.
How is KV Cache calculated for GLM-4-32B-0414?
GLM-4-32B-0414 uses Dense-GQA with 61 transformer layers, 48 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.91 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.95 GB.