GLM 5.3 Flash EXL3 K2 — Hardware Requirements & GPU Compatibility
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GLM 5.3 Flash EXL3 K2 is a 48.9B-parameter open language model from vcruz305 in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 31.12 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
4 likesSpecifications
- Publisher
- vcruz305
- Family
- GLM 5
- Parameters
- 48.9B
- Architecture
- Glm5NextForConditionalGeneration
- 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 Flash EXL3 K2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 22.6 GB | 794.2 GB | 20.76 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 25.6 GB | 797.2 GB | 23.82 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 31.1 GB | 802.7 GB | 29.31 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 36.6 GB | 808.2 GB | 34.81 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 42.1 GB | 813.7 GB | 40.30 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 50.7 GB | 822.3 GB | 48.85 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 99.5 GB | 871.1 GB | 97.71 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 Flash EXL3 K2?
Q4_K_M · 31.1 GBGLM 5.3 Flash EXL3 K2 (Q4_K_M) requires 31.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 41+ GB is recommended. Using the full 1049K context window can add up to 771.6 GB, bringing total usage to 802.7 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run GLM 5.3 Flash EXL3 K2?
Q4_K_M · 31.1 GB31 devices with unified memory can run GLM 5.3 Flash EXL3 K2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does GLM 5.3 Flash EXL3 K2 need?
GLM 5.3 Flash EXL3 K2 requires 31.1 GB of VRAM at Q4_K_M, or 99.5 GB at BF16. Full 1049K context adds up to 771.6 GB (802.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 48.9B × 4.8 bits ÷ 8 = 29.3 GB
KV Cache + Overhead ≈ 1.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 773.4 GB (at full 1049K context)
VRAM usage by quantization
Q4_K_M31.1 GBQ4_K_M + full context802.7 GB- Can NVIDIA GeForce RTX 4090 run GLM 5.3 Flash EXL3 K2?
Yes, at Q2_K (22.6 GB) or lower. Higher quantizations like Q3_K_M (25.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for GLM 5.3 Flash EXL3 K2?
For GLM 5.3 Flash EXL3 K2, Q4_K_M (31.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (36.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 22.6 GB.
VRAM requirement by quantization
Q2_K22.6 GBQ4_K_M ★31.1 GBQ5_K_M36.6 GBQ6_K42.1 GBQ8_050.7 GBBF1699.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GLM 5.3 Flash EXL3 K2 on a Mac?
GLM 5.3 Flash EXL3 K2 requires at least 22.6 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 Flash EXL3 K2 locally?
Yes — GLM 5.3 Flash EXL3 K2 can run locally on consumer hardware. At Q4_K_M quantization it needs 31.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GLM 5.3 Flash EXL3 K2?
At Q4_K_M, GLM 5.3 Flash EXL3 K2 can reach ~154 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 B200 → 8000 ÷ 31.1 × 0.65 = ~167 tok/s
Estimated speed at Q4_K_M (31.1 GB)
~167 tok/s~167 tok/s~154 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 Flash EXL3 K2?
At Q4_K_M, the download is about 29.31 GB. The full-precision BF16 version is 97.71 GB. The smallest option (Q2_K) is 20.76 GB.
- Which GPUs can run GLM 5.3 Flash EXL3 K2?
1 consumer GPU can run GLM 5.3 Flash EXL3 K2 at Q4_K_M (31.1 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run GLM 5.3 Flash EXL3 K2?
35 devices with unified memory can run GLM 5.3 Flash EXL3 K2 at Q4_K_M (31.1 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.