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.
13.2M downloads
3.3K likesSpecifications
- Publisher
- OpenAI
- Parameters
- 137M
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2022-03-02
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Gpt2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.1 GB | — | 0.06 GB | 2-bit quantization with K-quant improvements |
| 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 |
| Q4_0 | 4.00 | 0.1 GB | — | 0.07 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.1 GB | — | 0.08 GB | 4-bit medium quantization — most popular sweet spot |
| 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 |
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 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. 50 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 GB59 devices with unified memory can run Gpt2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Gpt2
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Gpt2 need?
Gpt2 requires 0.1 GB of VRAM at Q4_K_M, or 0.3 GB at BF16.
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 GBQ3_K_S0.1 GBIQ4_XS0.1 GBQ4_K_M ★0.1 GBQ5_K_S0.1 GBBF160.3 GB★ 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 ~48889 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 0.1 × 0.65 = ~57778 tok/s
Estimated speed at Q4_K_M (0.1 GB)
~57778 tok/s~7280 tok/s~57778 tok/s~48889 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 BF16 version is 0.27 GB. The smallest option (IQ3_XS) is 0.06 GB.
- Which GPUs can run Gpt2?
50 consumer GPUs can run Gpt2 at Q4_K_M (0.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gpt2?
59 devices with unified memory can run Gpt2 at Q4_K_M (0.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.