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 |
| Q4_K_M | 4.80 | 0.1 GB | — | 0.05 GB | 4-bit medium quantization — most popular sweet spot |
| 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 GBQ3_K_L0.1 GBQ4_K_S0.1 GBQ4_K_M ★0.1 GBQ5_K_M0.1 GBQ8_00.1 GB★ 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)
~48583 tok/s~10920 tok/s~36313 tok/s~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.
- Which GPUs can run Distilgpt2?
35 consumer GPUs can run Distilgpt2 at Q4_K_M (0.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Distilgpt2?
33 devices with unified memory can run Distilgpt2 at Q4_K_M (0.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.