Gpt2 Medium — Hardware Requirements & GPU Compatibility
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GPT-2 Medium scales the original GPT-2 architecture to 380 million parameters, offering noticeably improved text generation quality over the base 137M variant while remaining extremely lightweight by current standards. It supports the same autoregressive language modeling tasks as its smaller and larger siblings. Like all GPT-2 variants, it runs comfortably on virtually any modern hardware including CPU-only setups, making it an accessible option for learning, prototyping, and lightweight text generation experiments without needing a dedicated GPU.
716.0K downloads
196 likesSpecifications
- Publisher
- OpenAI
- Parameters
- 380M
- 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 Medium Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_XS | 3.30 | 0.2 GB | — | 0.16 GB | Importance-weighted 3-bit, extra small |
| Q3_K_S | 3.50 | 0.2 GB | — | 0.17 GB | 3-bit small quantization |
| Q2_K | 3.40 | 0.2 GB | — | 0.16 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 0.2 GB | — | 0.16 GB | Importance-weighted 3-bit, small |
| IQ3_M | 3.60 | 0.2 GB | — | 0.17 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 0.2 GB | — | 0.19 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 0.2 GB | — | 0.19 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.2 GB | — | 0.20 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 0.2 GB | — | 0.21 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 0.3 GB | — | 0.23 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 0.3 GB | — | 0.26 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 0.3 GB | — | 0.27 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.3 GB | — | 0.31 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.4 GB | — | 0.38 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gpt2 Medium?
Q4_K_M · 0.3 GBGpt2 Medium (Q4_K_M) requires 0.3 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 Medium?
Q4_K_M · 0.3 GB33 devices with unified memory can run Gpt2 Medium, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Gpt2 Medium need?
Gpt2 Medium requires 0.3 GB of VRAM at Q4_K_M, or 0.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 380M × 4.8 bits ÷ 8 = 0.2 GB
VRAM usage by quantization
Q4_K_M0.3 GB- What's the best quantization for Gpt2 Medium?
For Gpt2 Medium, Q4_K_M (0.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.3 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.2 GB.
VRAM requirement by quantization
IQ3_XS0.2 GB~73%IQ3_S0.2 GB~75%IQ4_XS0.2 GB~87%Q4_K_M ★0.3 GB~89%Q5_K_S0.3 GB~92%Q8_00.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gpt2 Medium on a Mac?
Gpt2 Medium requires at least 0.2 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 Medium locally?
Yes — Gpt2 Medium can run locally on consumer hardware. At Q4_K_M quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gpt2 Medium?
At Q4_K_M, Gpt2 Medium can reach ~11660 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~2621 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 ÷ 0.3 × 0.55 = ~11660 tok/s
Estimated speed at Q4_K_M (0.3 GB)
AMD Instinct MI300X~11660 tok/sNVIDIA GeForce RTX 4090~2621 tok/sNVIDIA H100 SXM~8715 tok/sAMD Instinct MI250X~7209 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gpt2 Medium?
At Q4_K_M, the download is about 0.23 GB. The full-precision Q8_0 version is 0.38 GB. The smallest option (IQ3_XS) is 0.16 GB.