GLM 5.3 W4AFP8 — Hardware Requirements & GPU Compatibility
Chat
GLM 5.3 W4AFP8 is a 386.1B-parameter open language model from PhalaCloud in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 235.89 GB of VRAM — see which GPUs and Macs can run it below.
1.8K downloads
8 likesSpecifications
- Publisher
- PhalaCloud
- Family
- GLM 5
- Parameters
- 386.1B
- Architecture
- GlmMoeDsaForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-08-28
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does GLM 5.3 W4AFP8 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 168.3 GB | 2174.4 GB | 164.10 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 192.4 GB | 2198.6 GB | 188.23 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 235.9 GB | 2242.0 GB | 231.67 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 279.3 GB | 2285.4 GB | 275.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 322.8 GB | 2328.9 GB | 318.54 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 390.3 GB | 2396.4 GB | 386.11 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 776.5 GB | 2782.6 GB | 772.22 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 5.3 W4AFP8?
Q4_K_M · 235.9 GBGLM 5.3 W4AFP8 (Q4_K_M) requires 235.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 307+ GB is recommended. Using the full 1049K context window can add up to 2006.1 GB, bringing total usage to 2242.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run GLM 5.3 W4AFP8?
Q4_K_M · 235.9 GB3 devices with unified memory can run GLM 5.3 W4AFP8, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does GLM 5.3 W4AFP8 need?
GLM 5.3 W4AFP8 requires 235.9 GB of VRAM at Q4_K_M, or 776.5 GB at BF16. Full 1049K context adds up to 2006.1 GB (2242.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 386.1B × 4.8 bits ÷ 8 = 231.7 GB
KV Cache + Overhead ≈ 4.2 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2010.3 GB (at full 1049K context)
VRAM usage by quantization
Q4_K_M235.9 GBQ4_K_M + full context2242.0 GB- Can NVIDIA GeForce RTX 5090 run GLM 5.3 W4AFP8?
No — GLM 5.3 W4AFP8 requires at least 168.3 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for GLM 5.3 W4AFP8?
For GLM 5.3 W4AFP8, Q4_K_M (235.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (279.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 168.3 GB.
VRAM requirement by quantization
Q2_K168.3 GBQ4_K_M ★235.9 GBQ5_K_M279.3 GBQ6_K322.8 GBQ8_0390.3 GBBF16776.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GLM 5.3 W4AFP8 on a Mac?
GLM 5.3 W4AFP8 requires at least 168.3 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 5.3 W4AFP8 locally?
Yes — GLM 5.3 W4AFP8 can run locally on consumer hardware. At Q4_K_M quantization it needs 235.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GLM 5.3 W4AFP8?
At Q4_K_M, GLM 5.3 W4AFP8 can reach ~20 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B300 → 8000 ÷ 235.9 × 0.65 = ~22 tok/s
Estimated speed at Q4_K_M (235.9 GB)
~22 tok/s~20 tok/s~20 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GLM 5.3 W4AFP8?
At Q4_K_M, the download is about 231.67 GB. The full-precision BF16 version is 772.22 GB. The smallest option (Q2_K) is 164.10 GB.
- Which GPUs can run GLM 5.3 W4AFP8?
No single consumer GPU has enough VRAM to run GLM 5.3 W4AFP8 at Q4_K_M (235.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run GLM 5.3 W4AFP8?
4 devices with unified memory can run GLM 5.3 W4AFP8 at Q4_K_M (235.9 GB), including Mac Studio (M3 Ultra, 256GB), 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.