ERNIE 4.5 0.3B Paddle — Hardware Requirements & GPU Compatibility
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ERNIE 4.5 0.3B Paddle is a 361M-parameter open language model from Baidu in the ERNIE family. It supports a context window of up to 131,072 tokens. At BF16 it needs about 1.04 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 128K)Best on 24 GB (BF16 · ~630 tok/s · 128K)Macs from 8 GB (BF16 · ~69 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run ERNIE 4.5 0.3B Paddle
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve baidu/ERNIE-4.5-0.3B-Paddle --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (10 GB headroom) · BF16
- Generation speed
- ~225 tok/s
- generation speed
- Cost per 1M output tokens
- $0.10
- per 1M output tokens
How Much VRAM Does ERNIE 4.5 0.3B Paddle Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.0 GB | 1.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~630 tok/s |
Selected BF16
Brain floating point 161.2GBat 16K context
- Weights
- 0.7 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.8 GB free · ~630 tok/s
≥50 tok/s needs ≥80 GB/s · RTX 5090 ~1120, RTX 3090 Ti ~630
Which Hardware Can Run ERNIE 4.5 0.3B Paddle?
BF16 · 1.0 GBERNIE 4.5 0.3B Paddle (BF16) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 1.2 GB, bringing total usage to 2.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run ERNIE 4.5 0.3B Paddle, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.8 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~475Simulate output on RTX 3080RTX 2080 Ti~385Simulate output on RTX 2080 TiRTX 3070 Ti~380Simulate output on RTX 3070 TiM3*8 GB~69Simulate output on iMac 24" M3 (8 GB)Orin Nano~64Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~46Simulate output on iPhone 1720 GB
fits · 18.8 GB free · up to 128K ctx
1 GPU
RX 7900 XT~462Simulate output on RX 7900 XT32–40 GB
fits · 30.8 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1120Simulate output on RTX 5090M5 Max36 GB~310Simulate output on Mac Studio M5 Max (36 GB)M532 GB~103Simulate output on MacBook Air 13" M5 (32 GB)Orin~128Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~84Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to ERNIE 4.5 0.3B Paddle
ERNIE family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite 4.0 350MIBM | — | 1 GB | ~1150 | same size |
| SmolLM 360MHugging Face | — | 0.6 GB | ~1092 | same size |
| LFM2.5 350MLiquid AI | — | 0.8 GB | ~1130 | same size |
| Opt 350MMeta | — | 0.8 GB | ~851 | same size |
| Codegen 350M MonoSalesforce | — | 0.8 GB | ~851 | same size |
| Gpt2 MediumOpenAI | — | 0.3 GB | ~2621 | same size |
Specifications
- Publisher
- Baidu
- Family
- ERNIE
- Parameters
- 361M
- Architecture
- Ernie4_5_ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 103,424
- Release Date
- 2025-06-29
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does ERNIE 4.5 0.3B Paddle need?
ERNIE 4.5 0.3B Paddle requires 1.0 GB of VRAM at BF16. Full 131K context adds up to 1.2 GB (2.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 361M × 16 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.3 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 ≈ 1.5 GB (at full 131K context)
VRAM usage by quantization
Can I run ERNIE 4.5 0.3B Paddle on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run ERNIE 4.5 0.3B Paddle. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 1.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run ERNIE 4.5 0.3B Paddle locally?
Yes — ERNIE 4.5 0.3B Paddle can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is ERNIE 4.5 0.3B Paddle?
At BF16, ERNIE 4.5 0.3B Paddle can reach ~4615 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~630 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 ÷ 1.04 × 0.65 = ~5000 tok/s
Estimated speed at BF16 (1.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of ERNIE 4.5 0.3B Paddle?
At BF16, the download is about 0.72 GB.
Which GPUs can run ERNIE 4.5 0.3B Paddle?
52 consumer GPUs can run ERNIE 4.5 0.3B Paddle at BF16. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run ERNIE 4.5 0.3B Paddle?
163 devices with unified memory can run ERNIE 4.5 0.3B Paddle at BF16 (1.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.