GLM 5.3 Flash MXFP4 Mixed CT AutoRound — Hardware Requirements & GPU Compatibility
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GLM 5.3 Flash MXFP4 Mixed CT AutoRound is a 321.3B-parameter open language model from INCModel3 in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 194.60 GB of VRAM — see which GPUs and Macs can run it below.
GLM 5.3 Flash MXFP4 Mixed CT AutoRound runs on a 256 GB+ Mac or a datacenter GPU
Best local pick → Mac Studio (M3 Ultra, 256GB) · Q4_K_M · 205.2 GB · ~3 tok/s
Macs from 192 GB (Q3_K_M · ~4 tok/s)
MIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GLM 5.3 Flash MXFP4 Mixed CT AutoRound
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve INCModel3/GLM-5.3-Flash-MXFP4-Mixed-CT-AutoRound --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on B300 (288 GB) (82 GB headroom) · Q4_K_M
- Generation speed
- ~27 tok/s
- generation speed
- Cost per 1M output tokens
- $72.20
- per 1M output tokens
How Much VRAM Does GLM 5.3 Flash MXFP4 Mixed CT AutoRound Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 138.4 GB | 161 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on B200 192 GB | |
| 158.4 GB | 181.1 GB | 3-bit medium — 3-bit medium quantization | Runs on B200 192 GB | |
| 194.6 GB | 217.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on Instinct MI325X 256 GB | |
| 230.8 GB | 253.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on Instinct MI325X 256 GB | |
| 266.9 GB | 289.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on Instinct MI350X 288 GB | |
| 323.1 GB | 345.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 5× H100 SXM 80 GB | |
| 644.5 GB | 667.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 6× H200 SXM 141 GB |
Selected Q4_K_M
4-bit medium quantization205.2GBat 16K context
- Weights
- 192.8 GB
- KV cache
- 12.1 GB
- Overhead
- 0.3 GB
Server-class: ~205.2 GB at 16K context · one Instinct MI325X 256 GB
Which Hardware Can Run GLM 5.3 Flash MXFP4 Mixed CT AutoRound?
Q4_K_M · 194.6 GBGLM 5.3 Flash MXFP4 Mixed CT AutoRound (Q4_K_M) requires 194.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 253+ GB is recommended. Using the full 1049K context window can add up to 771.6 GB, bringing total usage to 966.2 GB. No consumer GPU has enough memory. 2 devices with unified memory can run GLM 5.3 Flash MXFP4 Mixed CT AutoRound, including Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 256GB).
192+ GB
Q4_K_M · best fit Q3_K_M (169.0 GB)
6 GPUs · 1 Mac class · 2 device families
B200~33Simulate output on B200B300~33Simulate output on B300Instinct MI350X~30Simulate output on Instinct MI350XM3 Ultra256–512 GB~4Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~110Simulate output on NVIDIA DGX H1008x A100 SXM4~67Simulate output on NVIDIA DGX A100 640GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 128 GB: too small for this model (needs ≥ 148.9 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
Alternatives to GLM 5.3 Flash MXFP4 Mixed CT AutoRound
GLM 5 family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| IQuest Q1IQuestLab | — | 195.4 GB | —needs > 24 GB | same size |
| MiMo V2.6 Flash MOPDXiaomi | — | 186.5 GB | —needs > 24 GB | same size |
| DeepSeek V4 Flash Vision ExpDeepSeek | — | 183.3 GB | —needs > 24 GB | same size |
| Hy3Tencent | — | 182.3 GB | —needs > 24 GB | same size |
| Step3StepFun | — | 219.1 GB | —needs > 24 GB | same size |
| Motif 3Motif-Technologies | — | 632.8 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- INCModel3
- Family
- GLM 5
- Parameters
- 321.3B
- Architecture
- Glm5NextForConditionalGeneration
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-08-31
- License
- MIT
Frequently Asked Questions
How much VRAM does GLM 5.3 Flash MXFP4 Mixed CT AutoRound need?
GLM 5.3 Flash MXFP4 Mixed CT AutoRound requires 194.6 GB of VRAM at Q4_K_M, or 644.5 GB at BF16. Full 1049K context adds up to 771.6 GB (966.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 321.3B × 4.8 bits ÷ 8 = 192.8 GB
KV Cache + Overhead ≈ 1.8 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 773.4 GB (at full 1049K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run GLM 5.3 Flash MXFP4 Mixed CT AutoRound?
No — GLM 5.3 Flash MXFP4 Mixed CT AutoRound requires at least 138.4 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for GLM 5.3 Flash MXFP4 Mixed CT AutoRound?
For GLM 5.3 Flash MXFP4 Mixed CT AutoRound, Q4_K_M (194.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (230.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 138.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 5.3 Flash MXFP4 Mixed CT AutoRound on a Mac?
Yes — Mac Pro M2 Ultra (192 GB) and 3 other Macs can run GLM 5.3 Flash MXFP4 Mixed CT AutoRound, the smallest at Q3_K_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 194.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 256GB).
Can I run GLM 5.3 Flash MXFP4 Mixed CT AutoRound locally?
Yes — GLM 5.3 Flash MXFP4 Mixed CT AutoRound can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GLM 5.3 Flash MXFP4 Mixed CT AutoRound?
At Q4_K_M, GLM 5.3 Flash MXFP4 Mixed CT AutoRound can reach ~25 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 ÷ 194.6 × 0.65 = ~27 tok/s
Estimated speed at Q4_K_M (194.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GLM 5.3 Flash MXFP4 Mixed CT AutoRound?
At Q4_K_M, the download is about 192.79 GB. The full-precision BF16 version is 642.65 GB. The smallest option (Q2_K) is 136.56 GB.
Which GPUs can run GLM 5.3 Flash MXFP4 Mixed CT AutoRound?
No single consumer GPU has enough VRAM to run GLM 5.3 Flash MXFP4 Mixed CT AutoRound at Q4_K_M (194.6 GB). Multi-GPU or professional hardware is required.
Which devices can run GLM 5.3 Flash MXFP4 Mixed CT AutoRound?
2 devices with unified memory can run GLM 5.3 Flash MXFP4 Mixed CT AutoRound at Q4_K_M (194.6 GB), including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.