GLM 5.2 FP8 — Hardware Requirements & GPU Compatibility
Chat
GLM 5.2 FP8 is a 753.4B-parameter open language model from Z.ai in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 456.25 GB of VRAM — see which GPUs and Macs can run it below.
2.9M downloads
225 likesSpecifications
- Publisher
- Z.ai
- Family
- GLM 5
- Parameters
- 753.4B
- Architecture
- GlmMoeDsaForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-06-16
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does GLM 5.2 FP8 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 324.4 GB | 2330.5 GB | 320.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 371.5 GB | 2377.6 GB | 367.27 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 456.3 GB | 2462.4 GB | 452.03 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 541.0 GB | 2547.1 GB | 536.78 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 625.8 GB | 2631.9 GB | 621.54 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 757.6 GB | 2763.7 GB | 753.38 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1511.0 GB | 3517.1 GB | 1506.75 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run GLM 5.2 FP8?
Q4_K_M · 456.3 GBGLM 5.2 FP8 (Q4_K_M) requires 456.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 594+ GB is recommended. Using the full 1049K context window can add up to 2006.1 GB, bringing total usage to 2462.4 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run GLM 5.2 FP8?
Q4_K_M · 456.3 GB2 devices with unified memory can run GLM 5.2 FP8, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does GLM 5.2 FP8 need?
GLM 5.2 FP8 requires 456.3 GB of VRAM at Q4_K_M, or 1511.0 GB at BF16. Full 1049K context adds up to 2006.1 GB (2462.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 753.4B × 4.8 bits ÷ 8 = 452 GB
KV Cache + Overhead ≈ 4.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2010.4 GB (at full 1049K context)
VRAM usage by quantization
Q4_K_M456.3 GBQ4_K_M + full context2462.4 GB- Can NVIDIA GeForce RTX 5090 run GLM 5.2 FP8?
No — GLM 5.2 FP8 requires at least 324.4 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for GLM 5.2 FP8?
For GLM 5.2 FP8, Q4_K_M (456.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (541.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 324.4 GB.
VRAM requirement by quantization
Q2_K324.4 GBQ4_K_M ★456.3 GBQ5_K_M541.0 GBQ6_K625.8 GBQ8_0757.6 GBBF161511.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GLM 5.2 FP8 on a Mac?
GLM 5.2 FP8 requires at least 324.4 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run GLM 5.2 FP8 locally?
Yes — GLM 5.2 FP8 can run locally on consumer hardware. At Q4_K_M quantization it needs 456.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of GLM 5.2 FP8?
At Q4_K_M, the download is about 452.03 GB. The full-precision BF16 version is 1506.75 GB. The smallest option (Q2_K) is 320.18 GB.
- Which GPUs can run GLM 5.2 FP8?
No single consumer GPU has enough VRAM to run GLM 5.2 FP8 at Q4_K_M (456.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run GLM 5.2 FP8?
3 devices with unified memory can run GLM 5.2 FP8 at Q4_K_M (456.3 GB), including Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.