Distilgpt2 — Hardware Requirements & GPU Compatibility
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DistilGPT-2 is a distilled version of OpenAI's GPT-2 model, compressed to just 88 million parameters while retaining much of the original model's text generation ability. Created using knowledge distillation techniques, it offers significantly faster inference than the full GPT-2 with only a modest reduction in output quality. This model is one of the lightest autoregressive language models available and can run on virtually any hardware, including CPUs. It is a practical choice for educational projects, quick prototyping, and applications where inference speed and minimal resource usage are more important than state-of-the-art generation quality.
2.3M downloads
618 likesSpecifications
- Publisher
- distilbert
- Parameters
- 88M
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2024-02-19
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Distilgpt2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.0 GB | — | 0.04 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.0 GB | — | 0.04 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.1 GB | — | 0.04 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 0.1 GB | — | 0.05 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.1 GB | — | 0.05 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 0.1 GB | — | 0.05 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 0.1 GB | — | 0.05 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 0.1 GB | — | 0.06 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 0.1 GB | — | 0.06 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.1 GB | — | 0.07 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.1 GB | — | 0.09 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Distilgpt2?
Q4_K_M · 0.1 GBDistilgpt2 (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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Distilgpt2?
Q4_K_M · 0.1 GB33 devices with unified memory can run Distilgpt2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Distilgpt2 need?
Distilgpt2 requires 0.1 GB of VRAM at Q4_K_M, or 0.1 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 88M × 4.8 bits ÷ 8 = 0.1 GB
VRAM usage by quantization
Q4_K_M0.1 GB- What's the best quantization for Distilgpt2?
For Distilgpt2, Q4_K_M (0.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.0 GB.
VRAM requirement by quantization
Q2_K0.0 GB~75%Q3_K_L0.1 GB~86%Q4_K_S0.1 GB~88%Q4_K_M ★0.1 GB~89%Q5_K_M0.1 GB~92%Q8_00.1 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Distilgpt2 on a Mac?
Distilgpt2 requires at least 0.0 GB at Q2_K, 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 Distilgpt2 locally?
Yes — Distilgpt2 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 Distilgpt2?
At Q4_K_M, Distilgpt2 can reach ~48583 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~10920 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 ÷ 0.1 × 0.55 = ~48583 tok/s
Estimated speed at Q4_K_M (0.1 GB)
AMD Instinct MI300X~48583 tok/sNVIDIA GeForce RTX 4090~10920 tok/sNVIDIA H100 SXM~36313 tok/sAMD Instinct MI250X~30037 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Distilgpt2?
At Q4_K_M, the download is about 0.05 GB. The full-precision Q8_0 version is 0.09 GB. The smallest option (Q2_K) is 0.04 GB.