Gpt2 Mini — Hardware Requirements & GPU Compatibility
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Gpt2 Mini is a 39M-parameter open language model from erwanf. It supports a context window of up to 512 tokens. At Q4_K_M it needs about 0.03 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M)Best on 24 GB (Q4_K_M · ~21840 tok/s)Macs from 8 GB (Q4_K_M · ~2389 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gpt2 Mini
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-mini-i1-GGUF:Q4_K_MQ4_K_M · 512 context
Download: gpt2-mini.i1-Q4_K_M.gguf0.03 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 3060 12GB (12 GB headroom) · Q4_K_M
- Generation speed
- ~7800 tok/s
- generation speed
- Cost per 1M output tokens
- $0.0022
- per 1M output tokens
How Much VRAM Does Gpt2 Mini Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.0 GB | — | 3-bit medium — 3-bit medium quantization | Fits: — context · ~32760 tok/s | |
| 0.0 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: — context · ~32760 tok/s | |
| 0.0 GB | — | 3-bit small — 3-bit small quantization | Fits: — context · ~32760 tok/s | |
| 0.0 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: — context · ~32760 tok/s | |
| 0.0 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: — context · ~21840 tok/s | |
| 0.0 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: — context · ~21840 tok/s | |
| 0.0 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: — context · ~16380 tok/s | |
| 0.0 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: — context · ~16380 tok/s |
Selected Q4_K_M
4-bit medium quantization0.0GBat 1K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 24.0 GB free · ~21840 tok/s
≥50 tok/s needs ≥2 GB/s · RTX 5090 ~38827, RTX 3090 Ti ~21840
Which Hardware Can Run Gpt2 Mini?
Q4_K_M · 0.0 GBGpt2 Mini (Q4_K_M) requires 0.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ 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 Mini, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 8.0 GB free · up to — ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~16473Simulate output on RTX 3080RTX 2080 Ti~13347Simulate output on RTX 2080 TiRTX 3070 Ti~13180Simulate output on RTX 3070 TiM3*8 GB~2389Simulate output on iMac 24" M3 (8 GB)Orin Nano~2210Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~1591Simulate output on iPhone 1720 GB
fits · 20.0 GB free · up to — ctx
1 GPU
RX 7900 XT~16000Simulate output on RX 7900 XT32–40 GB
fits · 32.0 GB free · up to — ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~38827Simulate output on RTX 5090M5 Max36 GB~10733Simulate output on Mac Studio M5 Max (36 GB)M532 GB~3584Simulate output on MacBook Air 13" M5 (32 GB)Orin~4437Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~2925Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Specifications
- Publisher
- erwanf
- Parameters
- 39M
- Architecture
- GPT2LMHeadModel
- Context Length
- 512 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2024-06-23
- License
- MIT
Frequently Asked Questions
How much VRAM does Gpt2 Mini need?
Gpt2 Mini requires 0.0 GB of VRAM at Q4_K_M, or 0.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 39M × 4.8 bits ÷ 8 = 0 GB
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 Mini?
For Gpt2 Mini, Q4_K_M (0.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gpt2 Mini on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gpt2 Mini. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gpt2 Mini locally?
Yes — Gpt2 Mini can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gpt2 Mini?
At Q4_K_M, Gpt2 Mini can reach ~160000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~21840 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 ÷ 0.03 × 0.65 = ~173333 tok/s
Estimated speed at Q4_K_M (0.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gpt2 Mini?
At Q4_K_M, the download is about 0.02 GB. The full-precision BF16 version is 0.08 GB. The smallest option (IQ2_XXS) is 0.01 GB.
Which GPUs can run Gpt2 Mini?
52 consumer GPUs can run Gpt2 Mini 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 Mini?
163 devices with unified memory can run Gpt2 Mini at Q4_K_M (0.0 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.