GLM 4.7 Flash REAP 23B A3B — Hardware Requirements & GPU Compatibility
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GLM 4.7 Flash REAP 23B A3B is a 23.0B-parameter open language model from Cerebras in the GLM 4 family. It supports a context window of up to 202,752 tokens. At Q4_K_M it needs about 14.89 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
78 likesSpecifications
- Publisher
- Cerebras
- Family
- GLM 4
- Parameters
- 23.0B
- Architecture
- Glm4MoeLiteForCausalLM
- Context Length
- 202,752 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-01-23
- License
- MIT
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HuggingFace
How Much VRAM Does GLM 4.7 Flash REAP 23B A3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 10.9 GB | 88.1 GB | 9.77 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 11.2 GB | 88.4 GB | 10.06 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 12.3 GB | 89.6 GB | 11.21 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 12.6 GB | 89.9 GB | 11.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 14.9 GB | 92.2 GB | 13.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 17.5 GB | 94.8 GB | 16.38 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 20.1 GB | 97.3 GB | 18.97 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 24.1 GB | 101.4 GB | 23.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run GLM 4.7 Flash REAP 23B A3B?
Q4_K_M · 14.9 GBGLM 4.7 Flash REAP 23B A3B (Q4_K_M) requires 14.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 203K context window can add up to 77.3 GB, bringing total usage to 92.2 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run GLM 4.7 Flash REAP 23B A3B?
Q4_K_M · 14.9 GB47 devices with unified memory can run GLM 4.7 Flash REAP 23B A3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomWhere to Download GLM 4.7 Flash REAP 23B A3B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does GLM 4.7 Flash REAP 23B A3B need?
GLM 4.7 Flash REAP 23B A3B requires 14.9 GB of VRAM at Q4_K_M, or 47.1 GB at BF16. Full 203K context adds up to 77.3 GB (92.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 23.0B × 4.8 bits ÷ 8 = 13.8 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 78.4 GB (at full 203K context)
VRAM usage by quantization
Q4_K_M14.9 GBQ4_K_M + full context92.2 GB- Can NVIDIA GeForce RTX 4090 run GLM 4.7 Flash REAP 23B A3B?
Yes, at Q6_K (20.1 GB) or lower. Higher quantizations like Q8_0 (24.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for GLM 4.7 Flash REAP 23B A3B?
For GLM 4.7 Flash REAP 23B A3B, Q4_K_M (14.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (16.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 7.4 GB.
VRAM requirement by quantization
IQ2_XXS7.4 GBQ3_K_S11.2 GBQ4_114.0 GBQ4_K_M ★14.9 GBQ5_K_S16.9 GBBF1647.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GLM 4.7 Flash REAP 23B A3B on a Mac?
GLM 4.7 Flash REAP 23B A3B requires at least 7.4 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 4.7 Flash REAP 23B A3B locally?
Yes — GLM 4.7 Flash REAP 23B A3B can run locally on consumer hardware. At Q4_K_M quantization it needs 14.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GLM 4.7 Flash REAP 23B A3B?
At Q4_K_M, GLM 4.7 Flash REAP 23B A3B can reach ~296 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~44 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 14.9 × 0.65 = ~349 tok/s
Estimated speed at Q4_K_M (14.9 GB)
~349 tok/s~44 tok/s~349 tok/s~296 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GLM 4.7 Flash REAP 23B A3B?
At Q4_K_M, the download is about 13.80 GB. The full-precision BF16 version is 45.99 GB. The smallest option (IQ2_XXS) is 6.32 GB.
- Which GPUs can run GLM 4.7 Flash REAP 23B A3B?
26 consumer GPUs can run GLM 4.7 Flash REAP 23B A3B at Q4_K_M (14.9 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run GLM 4.7 Flash REAP 23B A3B?
49 devices with unified memory can run GLM 4.7 Flash REAP 23B A3B at Q4_K_M (14.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.