Gpt2 Xl — Hardware Requirements & GPU Compatibility
Chat
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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M)Best on 24 GB (Q4_K_M · ~618 tok/s)Macs from 8 GB (Q4_K_M · ~68 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gpt2 Xl
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
ollama run hf.co/mradermacher/gpt2-xl-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 22.9 GB free · 1K context
Download: gpt2-xl.Q4_K_M.gguf1.19 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~560 tok/s
- generation speed
- Cost per 1M output tokens
- $0.061
- per 1M output tokens
How Much VRAM Does Gpt2 Xl Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.8 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: — context · ~874 tok/s | |
| 0.8 GB | — | 3-bit small — 3-bit small quantization | Fits: — context · ~851 tok/s | |
| 0.9 GB | — | 3-bit medium — 3-bit medium quantization | Fits: — context · ~762 tok/s | |
| 0.9 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: — context · ~745 tok/s | |
| 1.1 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: — context · ~618 tok/s | |
| 1.3 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: — context · ~520 tok/s | |
| 1.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: — context · ~449 tok/s | |
| 1.8 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: — context · ~370 tok/s |
Selected Q4_K_M
4-bit medium quantization1.1GBat 1K context
- Weights
- 0.5 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.9 GB free · ~618 tok/s
≥50 tok/s needs ≥81 GB/s · RTX 5090 ~1099, RTX 3090 Ti ~618
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gpt2 Xl, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.9 GB free · up to — ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~466Simulate output on RTX 3080RTX 2080 Ti~378Simulate output on RTX 2080 TiRTX 3070 Ti~373Simulate output on RTX 3070 TiM3*8 GB~68Simulate output on iMac 24" M3 (8 GB)Orin Nano~63Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~45Simulate output on iPhone 1720 GB
fits · 18.9 GB free · up to — ctx
1 GPU
RX 7900 XT~453Simulate output on RX 7900 XT32–40 GB
fits · 30.9 GB free · up to — ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1099Simulate output on RTX 5090M5 Max36 GB~304Simulate output on Mac Studio M5 Max (36 GB)M532 GB~101Simulate output on MacBook Air 13" M5 (32 GB)Orin~126Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~83Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Gpt2 Xl?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
The 6 boards that weigh most in the llmrun Score, of 7 with a result for this model; badges show who ran the test.
- METR Time Horizons
Epoch AI · Nov 2019Lab-reported
Lab-reported10.1%
#9/9 open
- HellaSwag
Epoch AI · Nov 2019Lab-reported
Lab-reported40.0%
#47/47 open
- ARC Challenge
Epoch AI · Nov 2019Lab-reported
Lab-reported25.0%
#56/56 open
- WinoGrande
Epoch AI · Nov 2019Lab-reported
Lab-reported58.3%
#44/51 open
- BoolQ
Epoch AI · Nov 2019Lab-reported
Lab-reported61.8%
#36/38 open
- OpenBookQA
Epoch AI · Nov 2019Lab-reported
Lab-reported22.4%
#26/26 open
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM3 1.7BLiquid AI | — | 2.1 GB | ~455 | same size |
| MiniCPM V 4.7 1BOpenBMB | — | 1.6 GB | ~512 | same size |
| Granite 4.0 1BIBM | — | 2.6 GB | ~452 | same size |
| LFM2 VL 1.6BLiquid AI | — | 1.5 GB | ~496 | same size |
| Qwen Base Invoicev1.01 1.5BLaaP-ai | — | 1.7 GB | ~512 | same size |
| Qwen3 1.7B BaseAlibaba | — | 3.2 GB | ~417 | same size |
Specifications
- Publisher
- OpenAI
- Parameters
- 1.6B
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2022-03-02
- License
- MIT
Frequently Asked Questions
How much VRAM does Gpt2 Xl need?
Gpt2 Xl requires 1.1 GB of VRAM at Q4_K_M, or 3.5 GB at BF16.
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)
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 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 IQ2_XXS at 0.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gpt2 Xl on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gpt2 Xl. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gpt2 Xl locally?
Yes — Gpt2 Xl can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gpt2 Xl?
At Q4_K_M, Gpt2 Xl can reach ~4528 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 1.06 × 0.65 = ~4906 tok/s
Estimated speed at Q4_K_M (1.1 GB)
Real-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 BF16 version is 3.22 GB. The smallest option (IQ2_XXS) is 0.44 GB.
Which GPUs can run Gpt2 Xl?
52 consumer GPUs can run Gpt2 Xl 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 Gpt2 Xl?
163 devices with unified memory can run Gpt2 Xl at Q4_K_M (1.1 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.