Gpt2 — Hardware Requirements & GPU Compatibility
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GPT-2 is the landmark 2019 language model from OpenAI that helped ignite widespread interest in large-scale text generation. At only 137 million parameters it is tiny by modern standards, but it holds an important place in AI history as the model that was initially deemed too dangerous to release in full. Today GPT-2 runs effortlessly on virtually any hardware, including CPUs, making it ideal for educational purposes, experimentation, and understanding transformer fundamentals. It should not be expected to match the quality of modern instruction-tuned models, but it remains a useful teaching tool and conversation starter.
10.8M downloads
3.1K likesSpecifications
- Publisher
- OpenAI
- Parameters
- 137M
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2024-02-19
- License
- MIT
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HuggingFace
How Much VRAM Does Gpt2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_XS | 3.30 | 0.1 GB | — | 0.06 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 0.1 GB | — | 0.06 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 0.1 GB | — | 0.06 GB | Importance-weighted 3-bit, small |
| IQ3_M | 3.60 | 0.1 GB | — | 0.06 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 0.1 GB | — | 0.07 GB | 3-bit medium quantization |
| Q3_K_S | 3.50 | 0.1 GB | — | 0.06 GB | 3-bit small quantization |
| Q3_K_L | 4.10 | 0.1 GB | — | 0.07 GB | 3-bit large quantization |
| Q4_K_S | 4.50 | 0.1 GB | — | 0.08 GB | 4-bit small quantization |
| IQ4_XS | 4.30 | 0.1 GB | — | 0.07 GB | Importance-weighted 4-bit, compact |
| Q4_0 | 4.00 | 0.1 GB | — | 0.07 GB | 4-bit legacy quantization |
| Q4_1 | 4.50 | 0.1 GB | — | 0.08 GB | 4-bit legacy quantization with offset |
| Q5_0 | 5.00 | 0.1 GB | — | 0.09 GB | 5-bit legacy quantization |
| Q4_K_M | 4.80 | 0.1 GB | — | 0.08 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_1 | 5.50 | 0.1 GB | — | 0.09 GB | 5-bit legacy quantization with offset |
| Q5_K_S | 5.50 | 0.1 GB | — | 0.09 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 0.1 GB | — | 0.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.1 GB | — | 0.11 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.1 GB | — | 0.14 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gpt2?
Q4_K_M · 0.1 GBGpt2 (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 Gpt2?
Q4_K_M · 0.1 GB33 devices with unified memory can run Gpt2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Gpt2 need?
Gpt2 requires 0.1 GB of VRAM at Q4_K_M, or 0.1 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 137M × 4.8 bits ÷ 8 = 0.1 GB
VRAM usage by quantization
Q4_K_M0.1 GB- What's the best quantization for Gpt2?
For Gpt2, Q4_K_M (0.1 GB) offers the best balance of quality and VRAM usage. Q5_0 (0.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.1 GB.
VRAM requirement by quantization
IQ3_XS0.1 GB~73%Q3_K_M0.1 GB~83%Q4_00.1 GB~85%Q4_K_M ★0.1 GB~89%Q5_10.1 GB~92%Q8_00.1 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gpt2 on a Mac?
Gpt2 requires at least 0.1 GB at IQ3_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 Gpt2 locally?
Yes — Gpt2 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 Gpt2?
At Q4_K_M, Gpt2 can reach ~32389 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~7280 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 = ~32389 tok/s
Estimated speed at Q4_K_M (0.1 GB)
AMD Instinct MI300X~32389 tok/sNVIDIA GeForce RTX 4090~7280 tok/sNVIDIA H100 SXM~24209 tok/sAMD Instinct MI250X~20025 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gpt2?
At Q4_K_M, the download is about 0.08 GB. The full-precision Q8_0 version is 0.14 GB. The smallest option (IQ3_XS) is 0.06 GB.