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
- 2022-03-02
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Gpt2 Medium Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| 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 |
| Q3_K_M | 3.90 | 0.2 GB | — | 0.19 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.3 GB | — | 0.23 GB | 4-bit medium quantization — most popular sweet spot |
| 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 |
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 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. 50 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 GB59 devices with unified memory can run Gpt2 Medium, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Gpt2 Medium
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 Medium need?
Gpt2 Medium requires 0.3 GB of VRAM at Q4_K_M, or 0.8 GB at BF16.
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 GBIQ3_M0.2 GBIQ4_XS0.2 GBQ4_K_M ★0.3 GBQ5_K_M0.3 GBBF160.8 GB★ 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 ~17600 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 0.3 × 0.65 = ~20800 tok/s
Estimated speed at Q4_K_M (0.3 GB)
~20800 tok/s~2621 tok/s~20800 tok/s~17600 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 BF16 version is 0.76 GB. The smallest option (IQ3_XS) is 0.16 GB.
- Which GPUs can run Gpt2 Medium?
50 consumer GPUs can run Gpt2 Medium at Q4_K_M (0.3 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 Medium?
59 devices with unified memory can run Gpt2 Medium at Q4_K_M (0.3 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.