OneNexus·GLM 5·GlmMoeDsaForCausalLM

GLM 5.3 MXFP4 — Hardware Requirements & GPU Compatibility

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GLM 5.3 MXFP4 is a 389.5B-parameter open language model from OneNexus in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 237.94 GB of VRAM — see which GPUs and Macs can run it below.

679 downloads 3 likes1049K context
Based on GLM 5.3

Specifications

Publisher
OneNexus
Family
GLM 5
Parameters
389.5B
Architecture
GlmMoeDsaForCausalLM
Context Length
1,048,576 tokens
Vocabulary Size
154,880
Release Date
2026-08-29
License
Other

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How Much VRAM Does GLM 5.3 MXFP4 Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.40169.8 GB
Q3_K_Mest.3.90194.1 GB
Q4_K_Mest.4.80237.9 GB
Q5_K_Mest.5.70281.8 GB
Q6_Kest.6.60325.6 GB
Q8_0est.8.00393.8 GB
BF16est.16.00783.3 GB

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 MXFP4?

Q4_K_M · 237.9 GB

GLM 5.3 MXFP4 (Q4_K_M) requires 237.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 310+ GB is recommended. Using the full 1049K context window can add up to 2006.1 GB, bringing total usage to 2244.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run GLM 5.3 MXFP4?

Q4_K_M · 237.9 GB

3 devices with unified memory can run GLM 5.3 MXFP4, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Related Models

Frequently Asked Questions

How much VRAM does GLM 5.3 MXFP4 need?

GLM 5.3 MXFP4 requires 237.9 GB of VRAM at Q4_K_M, or 783.3 GB at BF16. Full 1049K context adds up to 2006.1 GB (2244.1 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 389.5B × 4.8 bits ÷ 8 = 233.7 GB

KV Cache + Overhead 4.2 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 2010.4 GB (at full 1049K context)

VRAM usage by quantization

237.9 GB
2244.1 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run GLM 5.3 MXFP4?

No — GLM 5.3 MXFP4 requires at least 169.8 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 MXFP4?

For GLM 5.3 MXFP4, Q4_K_M (237.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (281.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 169.8 GB.

VRAM requirement by quantization

Q2_K
169.8 GB
Q4_K_M
237.9 GB
Q5_K_M
281.8 GB
Q6_K
325.6 GB
Q8_0
393.8 GB
BF16
783.3 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run GLM 5.3 MXFP4 on a Mac?

GLM 5.3 MXFP4 requires at least 169.8 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 MXFP4 locally?

Yes — GLM 5.3 MXFP4 can run locally on consumer hardware. At Q4_K_M quantization it needs 237.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is GLM 5.3 MXFP4?

At Q4_K_M, GLM 5.3 MXFP4 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 B3008000 ÷ 237.9 × 0.65 = ~22 tok/s

Estimated speed at Q4_K_M (237.9 GB)

~22 tok/s
~20 tok/s
~20 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of GLM 5.3 MXFP4?

At Q4_K_M, the download is about 233.72 GB. The full-precision BF16 version is 779.05 GB. The smallest option (Q2_K) is 165.55 GB.

Which GPUs can run GLM 5.3 MXFP4?

No single consumer GPU has enough VRAM to run GLM 5.3 MXFP4 at Q4_K_M (237.9 GB). Multi-GPU or professional hardware is required.

Which devices can run GLM 5.3 MXFP4?

4 devices with unified memory can run GLM 5.3 MXFP4 at Q4_K_M (237.9 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.