GLM 5.3 F.U AnthraClaud Edition BF16 — Hardware Requirements & GPU Compatibility
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GLM 5.3 F.U AnthraClaud Edition BF16 is a 753.3B-parameter open language model from Blackfrost-Research in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 456.22 GB of VRAM — see which GPUs and Macs can run it below.
GLM 5.3 F.U AnthraClaud Edition BF16 runs on a 512 GB+ Mac or a multi-GPU server
Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 453.8 GB · ~17 tok/s
Macs from 512 GB (Q4_K_M · ~17 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GLM 5.3 F.U AnthraClaud Edition BF16
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16 --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Rent 7× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$18.83/hr
- est., whole server
- GPUs
- 7 GPUs
- 560 GB of VRAM
How Much VRAM Does GLM 5.3 F.U AnthraClaud Edition BF16 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 324.4 GB | 323.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 5× H100 SXM 80 GB | |
| 371.5 GB | 370.5 GB | 3-bit medium — 3-bit medium quantization | Runs on 6× H100 SXM 80 GB | |
| 456.2 GB | 455.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 7× H100 SXM 80 GB | |
| 541.0 GB | 540 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 8× H100 SXM 80 GB | |
| 625.7 GB | 624.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 5× H200 SXM 141 GB | |
| 757.6 GB | 756.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× H200 SXM 141 GB | |
| 1510.9 GB | 1509.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 3 nodes of 8× H100 SXM |
Selected Q4_K_M
4-bit medium quantization453.8GBat 16K context
- Weights
- 452.0 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Server-class: ~453.8 GB at 16K context · about 7× H100 SXM 80 GB
Which Hardware Can Run GLM 5.3 F.U AnthraClaud Edition BF16?
Q4_K_M · 456.2 GBGLM 5.3 F.U AnthraClaud Edition BF16 (Q4_K_M) requires 456.2 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 1049K context window can add up to 90.3 GB, bringing total usage to 546.5 GB. No consumer GPU has enough memory. 1 device with unified memory can run GLM 5.3 F.U AnthraClaud Edition BF16, including Mac Studio (M3 Ultra, 512GB).
Needs server-class hardware
GLM 5.3 F.U AnthraClaud Edition BF16 runs on a 512 GB+ Mac or a multi-GPU server · Q4_K_M needs ~453.8 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q4_K_M | 453.8 GB | ~17 tok/s | 512K ctx | Mac Studio → | Q4_K_M · 453.8 GB · ~17 tok/s · 512K ctx |
| 7× H100 SXM 80 GB · 560 GB | Q4_K_M | 453.8 GB | — | 512K ctx | H100 SXM → | Q4_K_M · 453.8 GB · 512K ctx |
| 4× H200 SXM 141 GB · 564 GB | Q4_K_M | 453.8 GB | — | 512K ctx | H200 SXM → | Q4_K_M · 453.8 GB · 512K ctx |
| 3× B200 192 GB · 576 GB | Q4_K_M | 453.8 GB | — | 512K ctx | B200 → | Q4_K_M · 453.8 GB · 512K ctx |
Also: Mac Studio (M3 Ultra, 512GB) · NVIDIA DGX A100 640GB · NVIDIA DGX H100
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 256 GB: too small for this model (needs ≥ 321.9 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
Alternatives to GLM 5.3 F.U AnthraClaud Edition BF16
GLM 5 family · 6 models →Similar models
3| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek V4.1 FlashDeepSeek | 129.5 | 458.4 GB | —needs > 24 GB | same size |
| Atria Dawn PreviewInternLM | — | 1508.4 GB | —needs > 24 GB | same size |
| Hy4 PreviewTencent | — | 1564.2 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Blackfrost-Research
- Family
- GLM 5
- Parameters
- 753.3B
- Active parameters
- 41.8B per token
- Architecture
- GlmMoeDsaForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-08-28
- License
- Other
Frequently Asked Questions
How much VRAM does GLM 5.3 F.U AnthraClaud Edition BF16 need?
GLM 5.3 F.U AnthraClaud Edition BF16 requires 456.2 GB of VRAM at Q4_K_M, or 1510.9 GB at BF16. Full 1049K context adds up to 90.3 GB (546.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 753.3B × 4.8 bits ÷ 8 = 452 GB
KV Cache + Overhead ≈ 4.2 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 ≈ 94.5 GB (at full 1049K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run GLM 5.3 F.U AnthraClaud Edition BF16?
No — GLM 5.3 F.U AnthraClaud Edition BF16 requires at least 324.4 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 F.U AnthraClaud Edition BF16?
For GLM 5.3 F.U AnthraClaud Edition BF16, Q4_K_M (456.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (541.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 324.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 5.3 F.U AnthraClaud Edition BF16 on a Mac?
Yes — Mac Studio (M3 Ultra, 512GB) and above can run GLM 5.3 F.U AnthraClaud Edition BF16. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 456.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run GLM 5.3 F.U AnthraClaud Edition BF16 locally?
Yes — GLM 5.3 F.U AnthraClaud Edition BF16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of GLM 5.3 F.U AnthraClaud Edition BF16?
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 GLM 5.3 F.U AnthraClaud Edition BF16?
No single consumer GPU has enough VRAM to run GLM 5.3 F.U AnthraClaud Edition BF16 at Q4_K_M (456.2 GB). Multi-GPU or professional hardware is required.
Which devices can run GLM 5.3 F.U AnthraClaud Edition BF16?
1 device with unified memory can run GLM 5.3 F.U AnthraClaud Edition BF16 at Q4_K_M (456.2 GB), including Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.