GLM 5 Abliterated — Hardware Requirements & GPU Compatibility
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263 downloads
9 likesSpecifications
- Publisher
- skyblanket
- Family
- GLM
- Parameters
- 753.9B
- Architecture
- GlmMoeDsaForCausalLM
- Context Length
- 202,752 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-02-22
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GLM 5 Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 324.6 GB | 709.4 GB | 320.39 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 334.0 GB | 718.8 GB | 329.82 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 371.7 GB | 756.5 GB | 367.51 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 381.2 GB | 765.9 GB | 376.93 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 456.5 GB | 841.3 GB | 452.32 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 541.4 GB | 926.1 GB | 537.13 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 626.2 GB | 1010.9 GB | 621.94 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 758.1 GB | 1142.8 GB | 753.86 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run GLM 5 Abliterated?
Q4_K_M · 456.5 GBGLM 5 Abliterated (Q4_K_M) requires 456.5 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 203K context window can add up to 384.7 GB, bringing total usage to 841.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run GLM 5 Abliterated?
Q4_K_M · 456.5 GB2 devices with unified memory can run GLM 5 Abliterated, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does GLM 5 Abliterated need?
GLM 5 Abliterated requires 456.5 GB of VRAM at Q4_K_M, or 758.1 GB at Q8_0. Full 203K context adds up to 384.7 GB (841.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 753.9B × 4.8 bits ÷ 8 = 452.3 GB
KV Cache + Overhead ≈ 4.2 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 389 GB (at full 203K context)
VRAM usage by quantization
Q4_K_M456.5 GBQ4_K_M + full context841.3 GB- Can NVIDIA GeForce RTX 5090 run GLM 5 Abliterated?
No — GLM 5 Abliterated requires at least 211.5 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for GLM 5 Abliterated?
For GLM 5 Abliterated, Q4_K_M (456.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (522.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 211.5 GB.
VRAM requirement by quantization
IQ2_XXS211.5 GB~53%Q3_K_S334.0 GB~77%Q4_1428.3 GB~88%Q4_K_M ★456.5 GB~89%Q5_K_S522.5 GB~92%Q8_0758.1 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run GLM 5 Abliterated on a Mac?
GLM 5 Abliterated requires at least 211.5 GB at IQ2_XXS, 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 Abliterated locally?
Yes — GLM 5 Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 456.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of GLM 5 Abliterated?
At Q4_K_M, the download is about 452.32 GB. The full-precision Q8_0 version is 753.86 GB. The smallest option (IQ2_XXS) is 207.31 GB.
- Which GPUs can run GLM 5 Abliterated?
No single consumer GPU has enough VRAM to run GLM 5 Abliterated at Q4_K_M (456.5 GB). Multi-GPU or professional hardware is required.
- Which devices can run GLM 5 Abliterated?
2 devices with unified memory can run GLM 5 Abliterated at Q4_K_M (456.5 GB), including 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.