ERNIE 4.5 21B A3B Thinking — Hardware Requirements & GPU Compatibility
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ERNIE 4.5 21B A3B Thinking is a 21.8B-parameter open language model from Baidu in the ERNIE family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 13.51 GB of VRAM — see which GPUs and Macs can run it below.
46.9K downloads
792 likesSpecifications
- Publisher
- Baidu
- Family
- ERNIE
- Parameters
- 21.8B
- Architecture
- Ernie4_5_MoeForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 103,424
- Release Date
- 2025-09-08
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does ERNIE 4.5 21B A3B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 9.7 GB | 17.1 GB | 9.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 11.1 GB | 18.5 GB | 10.64 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 13.5 GB | 20.9 GB | 13.10 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 16.0 GB | 23.4 GB | 15.55 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 18.4 GB | 25.8 GB | 18.01 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 22.2 GB | 29.6 GB | 21.83 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 44.1 GB | 51.5 GB | 43.65 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 ERNIE 4.5 21B A3B Thinking?
Q4_K_M · 13.5 GBERNIE 4.5 21B A3B Thinking (Q4_K_M) requires 13.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 18+ GB is recommended. Using the full 131K context window can add up to 7.4 GB, bringing total usage to 20.9 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 ERNIE 4.5 21B A3B Thinking?
Q4_K_M · 13.5 GB47 devices with unified memory can run ERNIE 4.5 21B A3B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does ERNIE 4.5 21B A3B Thinking need?
ERNIE 4.5 21B A3B Thinking requires 13.5 GB of VRAM at Q4_K_M, or 44.1 GB at BF16. Full 131K context adds up to 7.4 GB (20.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 21.8B × 4.8 bits ÷ 8 = 13.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M13.5 GBQ4_K_M + full context20.9 GB- Can NVIDIA GeForce RTX 4090 run ERNIE 4.5 21B A3B Thinking?
Yes, at Q8_0 (22.2 GB) or lower. Higher quantizations like BF16 (44.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for ERNIE 4.5 21B A3B Thinking?
For ERNIE 4.5 21B A3B Thinking, Q4_K_M (13.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (16.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 9.7 GB.
VRAM requirement by quantization
Q2_K9.7 GBQ4_K_M ★13.5 GBQ5_K_M16.0 GBQ6_K18.4 GBQ8_022.2 GBBF1644.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run ERNIE 4.5 21B A3B Thinking on a Mac?
ERNIE 4.5 21B A3B Thinking requires at least 9.7 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 ERNIE 4.5 21B A3B Thinking locally?
Yes — ERNIE 4.5 21B A3B Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 13.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is ERNIE 4.5 21B A3B Thinking?
At Q4_K_M, ERNIE 4.5 21B A3B Thinking can reach ~167 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~229 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 ÷ 13.5 × 0.65 = ~527 tok/s
Estimated speed at Q4_K_M (13.5 GB)
~527 tok/s~229 tok/s~527 tok/s~471 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of ERNIE 4.5 21B A3B Thinking?
At Q4_K_M, the download is about 13.10 GB. The full-precision BF16 version is 43.65 GB. The smallest option (Q2_K) is 9.28 GB.
- Which GPUs can run ERNIE 4.5 21B A3B Thinking?
26 consumer GPUs can run ERNIE 4.5 21B A3B Thinking at Q4_K_M (13.5 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 ERNIE 4.5 21B A3B Thinking?
49 devices with unified memory can run ERNIE 4.5 21B A3B Thinking at Q4_K_M (13.5 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.