Penclaw GLM 5.3 Abliterated — Hardware Requirements & GPU Compatibility
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Penclaw GLM 5.3 Abliterated is a 753.3B-parameter open language model from audnai in the GLM 5 family. At Q4_K_M it needs about 497.20 GB of VRAM — see which GPUs and Macs can run it below.
1.2K downloads
269 likesSpecifications
- Publisher
- audnai
- Family
- GLM 5
- Parameters
- 753.3B
- Release Date
- 2026-09-01
- License
- Other
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HuggingFace
How Much VRAM Does Penclaw GLM 5.3 Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 352.2 GB | — | 320.17 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 404.0 GB | — | 367.25 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 497.2 GB | — | 452.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 590.4 GB | — | 536.75 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 683.6 GB | — | 621.50 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 828.7 GB | — | 753.33 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1657.3 GB | — | 1506.66 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 Penclaw GLM 5.3 Abliterated?
Q4_K_M · 497.2 GBPenclaw GLM 5.3 Abliterated (Q4_K_M) requires 497.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 647+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Penclaw GLM 5.3 Abliterated?
Q4_K_M · 497.2 GB2 devices with unified memory can run Penclaw GLM 5.3 Abliterated, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Penclaw GLM 5.3 Abliterated need?
Penclaw GLM 5.3 Abliterated requires 497.2 GB of VRAM at Q4_K_M, or 1657.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 753.3B × 4.8 bits ÷ 8 = 452 GB
KV Cache + Overhead ≈ 45.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M497.2 GB- Can NVIDIA GeForce RTX 5090 run Penclaw GLM 5.3 Abliterated?
No — Penclaw GLM 5.3 Abliterated requires at least 352.2 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Penclaw GLM 5.3 Abliterated?
For Penclaw GLM 5.3 Abliterated, Q4_K_M (497.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (590.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 352.2 GB.
VRAM requirement by quantization
Q2_K352.2 GBQ4_K_M ★497.2 GBQ5_K_M590.4 GBQ6_K683.6 GBQ8_0828.7 GBBF161657.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Penclaw GLM 5.3 Abliterated on a Mac?
Penclaw GLM 5.3 Abliterated requires at least 352.2 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 Penclaw GLM 5.3 Abliterated locally?
Yes — Penclaw GLM 5.3 Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 497.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Penclaw GLM 5.3 Abliterated?
At Q4_K_M, the download is about 452.00 GB. The full-precision BF16 version is 1506.66 GB. The smallest option (Q2_K) is 320.17 GB.
- Which GPUs can run Penclaw GLM 5.3 Abliterated?
No single consumer GPU has enough VRAM to run Penclaw GLM 5.3 Abliterated at Q4_K_M (497.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Penclaw GLM 5.3 Abliterated?
3 devices with unified memory can run Penclaw GLM 5.3 Abliterated at Q4_K_M (497.2 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.