Gpt2 Xl — Hardware Requirements & GPU Compatibility
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GPT-2 XL is the largest variant of the GPT-2 family at 1.6 billion parameters, representing the full release of the model OpenAI originally withheld over safety concerns in 2019. It produces the most coherent and capable outputs of the GPT-2 lineup, though it remains far behind modern multi-billion-parameter instruction-tuned models. At its size, GPT-2 XL still runs easily on most consumer GPUs and even on CPUs with reasonable speed, making it useful for experimentation, fine-tuning projects, and as a baseline for comparing against newer architectures. It requires roughly 3 GB of VRAM at full precision.
200.1K downloads
376 likesSpecifications
- Publisher
- OpenAI
- Parameters
- 1.6B
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2024-02-19
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Gpt2 Xl Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_XS | 3.30 | 0.7 GB | — | 0.66 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 0.8 GB | — | 0.68 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 0.8 GB | — | 0.68 GB | Importance-weighted 3-bit, small |
| Q3_K_S | 3.50 | 0.8 GB | — | 0.70 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 0.8 GB | — | 0.72 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 0.9 GB | — | 0.78 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 0.9 GB | — | 0.82 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.9 GB | — | 0.86 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 1.0 GB | — | 0.90 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 1.1 GB | — | 0.96 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 1.2 GB | — | 1.11 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.3 GB | — | 1.15 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.5 GB | — | 1.33 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.8 GB | — | 1.61 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gpt2 Xl?
Q4_K_M · 1.1 GBGpt2 Xl (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 Xl?
Q4_K_M · 1.1 GB33 devices with unified memory can run Gpt2 Xl, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gpt2 Xl need?
Gpt2 Xl requires 1.1 GB of VRAM at Q4_K_M, or 1.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1.6B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.1 GB- What's the best quantization for Gpt2 Xl?
For Gpt2 Xl, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.2 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.7 GB.
VRAM requirement by quantization
IQ3_XS0.7 GB~73%Q3_K_S0.8 GB~77%IQ4_XS0.9 GB~87%Q4_K_M ★1.1 GB~89%Q5_K_S1.2 GB~92%Q8_01.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gpt2 Xl on a Mac?
Gpt2 Xl requires at least 0.7 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 Xl locally?
Yes — Gpt2 Xl can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gpt2 Xl?
At Q4_K_M, Gpt2 Xl can reach ~2750 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~618 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 ÷ 1.1 × 0.55 = ~2750 tok/s
Estimated speed at Q4_K_M (1.1 GB)
AMD Instinct MI300X~2750 tok/sNVIDIA GeForce RTX 4090~618 tok/sNVIDIA H100 SXM~2056 tok/sAMD Instinct MI250X~1700 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gpt2 Xl?
At Q4_K_M, the download is about 0.96 GB. The full-precision Q8_0 version is 1.61 GB. The smallest option (IQ3_XS) is 0.66 GB.