Openai GPT — Hardware Requirements & GPU Compatibility
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OpenAI GPT is the original 2018 transformer-based language model that started the GPT lineage, based on the paper "Improving Language Understanding by Generative Pre-Training." At just 120 million parameters, it is a historically significant model that demonstrated the power of unsupervised pretraining followed by supervised fine-tuning. This model is primarily of academic and historical interest today. It runs on essentially any hardware and can be useful for educational exploration of transformer architectures, but it should not be compared to modern instruction-tuned models in terms of practical capability.
231.9K downloads
288 likesSpecifications
- Publisher
- OpenAI
- Parameters
- 120M
- Architecture
- OpenAIGPTLMHeadModel
- Context Length
- 512 tokens
- Vocabulary Size
- 40,478
- Release Date
- 2024-02-19
- License
- MIT
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HuggingFace
How Much VRAM Does Openai GPT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 0.0 GB | — | 0.04 GB | Importance-weighted 2-bit, extra small |
| IQ2_XXS | 2.20 | 0.0 GB | — | 0.03 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 0.0 GB | — | 0.04 GB | Importance-weighted 2-bit, medium |
| IQ2_S | 2.50 | 0.0 GB | — | 0.04 GB | Importance-weighted 2-bit, small |
| IQ3_XS | 3.30 | 0.1 GB | — | 0.05 GB | Importance-weighted 3-bit, extra small |
| Q2_K_S | 3.20 | 0.1 GB | — | 0.05 GB | 2-bit small K-quant |
| IQ3_XXS | 3.10 | 0.1 GB | — | 0.05 GB | Importance-weighted 3-bit |
| Q3_K_S | 3.50 | 0.1 GB | — | 0.05 GB | 3-bit small quantization |
| Q2_K | 3.40 | 0.1 GB | — | 0.05 GB | 2-bit quantization with K-quant improvements |
| IQ3_M | 3.60 | 0.1 GB | — | 0.05 GB | Importance-weighted 3-bit, medium |
| IQ3_S | 3.40 | 0.1 GB | — | 0.05 GB | Importance-weighted 3-bit, small |
| Q3_K_M | 3.90 | 0.1 GB | — | 0.06 GB | 3-bit medium quantization |
| Q4_1 | 4.50 | 0.1 GB | — | 0.07 GB | 4-bit legacy quantization with offset |
| Q3_K_L | 4.10 | 0.1 GB | — | 0.06 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.1 GB | — | 0.06 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 0.1 GB | — | 0.07 GB | 4-bit small quantization |
| Q4_0 | 4.00 | 0.1 GB | — | 0.06 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.1 GB | — | 0.07 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.1 GB | — | 0.09 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_S | 5.50 | 0.1 GB | — | 0.08 GB | 5-bit small quantization |
| Q6_K | 6.60 | 0.1 GB | — | 0.10 GB | 6-bit quantization, very good quality |
Which GPUs Can Run Openai GPT?
Q4_K_M · 0.1 GBOpenai GPT (Q4_K_M) requires 0.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Openai GPT?
Q4_K_M · 0.1 GB33 devices with unified memory can run Openai GPT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Openai GPT need?
Openai GPT requires 0.1 GB of VRAM at Q4_K_M, or 0.1 GB at Q6_K.
VRAM = Weights + KV Cache + Overhead
Weights = 120M × 4.8 bits ÷ 8 = 0.1 GB
VRAM usage by quantization
Q4_K_M0.1 GB- What's the best quantization for Openai GPT?
For Openai GPT, Q4_K_M (0.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 0.0 GB.
VRAM requirement by quantization
IQ2_XS0.0 GB~57%Q2_K_S0.1 GB~71%IQ3_S0.1 GB~75%Q4_K_S0.1 GB~88%Q4_K_M ★0.1 GB~89%Q6_K0.1 GB~95%★ Recommended — best balance of quality and VRAM usage.
- Can I run Openai GPT on a Mac?
Openai GPT requires at least 0.0 GB at IQ2_XS, 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 Openai GPT locally?
Yes — Openai GPT can run locally on consumer hardware. At Q4_K_M quantization it needs 0.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Openai GPT?
At Q4_K_M, Openai GPT can reach ~36438 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~8190 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 0.1 × 0.55 = ~36438 tok/s
Estimated speed at Q4_K_M (0.1 GB)
AMD Instinct MI300X~36438 tok/sNVIDIA GeForce RTX 4090~8190 tok/sNVIDIA H100 SXM~27235 tok/sAMD Instinct MI250X~22528 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Openai GPT?
At Q4_K_M, the download is about 0.07 GB. The full-precision Q6_K version is 0.10 GB. The smallest option (IQ2_XS) is 0.04 GB.