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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M)Best on 24 GB (Q4_K_M · ~8190 tok/s)Macs from 8 GB (Q4_K_M · ~896 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Openai GPT
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve openai-community/openai-gpt --max-model-len 512vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~2925 tok/s
- generation speed
- Cost per 1M output tokens
- $0.0056
- per 1M output tokens
How Much VRAM Does Openai GPT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.1 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: — context · ~10920 tok/s | |
| 0.1 GB | — | 3-bit medium — 3-bit medium quantization | Fits: — context · ~10920 tok/s | |
| 0.1 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: — context · ~8190 tok/s | |
| 0.1 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: — context · ~7280 tok/s | |
| 0.1 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: — context · ~5956 tok/s | |
| 0.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: — context · ~5040 tok/s | |
| 0.3 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: — context · ~2520 tok/s |
Selected Q4_K_M
4-bit medium quantization0.1GBat 1K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.9 GB free · ~8190 tok/s
≥50 tok/s needs ≥6 GB/s · RTX 5090 ~14560, RTX 3090 Ti ~8190
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Openai GPT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.9 GB free · up to — ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~6177Simulate output on RTX 3080RTX 2080 Ti~5005Simulate output on RTX 2080 TiRTX 3070 Ti~4942Simulate output on RTX 3070 TiM3*8 GB~896Simulate output on iMac 24" M3 (8 GB)Orin Nano~829Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~597Simulate output on iPhone 1720 GB
fits · 19.9 GB free · up to — ctx
1 GPU
RX 7900 XT~6000Simulate output on RX 7900 XT32–40 GB
fits · 31.9 GB free · up to — ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~14560Simulate output on RTX 5090M5 Max36 GB~4025Simulate output on Mac Studio M5 Max (36 GB)M532 GB~1344Simulate output on MacBook Air 13" M5 (32 GB)Orin~1664Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~1097Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Specifications
- Publisher
- OpenAI
- Parameters
- 120M
- Architecture
- OpenAIGPTLMHeadModel
- Context Length
- 512 tokens
- Vocabulary Size
- 40,478
- Release Date
- 2022-03-02
- License
- MIT
Frequently Asked Questions
How much VRAM does Openai GPT need?
Openai GPT requires 0.1 GB of VRAM at Q4_K_M, or 0.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 120M × 4.8 bits ÷ 8 = 0.1 GB
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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 Q2_K at 0.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Openai GPT on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Openai GPT. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Openai GPT locally?
Yes — Openai GPT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Openai GPT?
At Q4_K_M, Openai GPT can reach ~60000 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 0.08 × 0.65 = ~65000 tok/s
Estimated speed at Q4_K_M (0.1 GB)
Real-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 BF16 version is 0.24 GB. The smallest option (Q2_K) is 0.05 GB.
Which GPUs can run Openai GPT?
52 consumer GPUs can run Openai GPT at Q4_K_M. 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 Openai GPT?
163 devices with unified memory can run Openai GPT at Q4_K_M (0.1 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.