Gpt2 Large — Hardware Requirements & GPU Compatibility
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Gpt2 Large is a 812M-parameter open language model from OpenAI. It supports a context window of up to 1,024 tokens. At Q4_K_M it needs about 0.54 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 · ~1213 tok/s)Macs from 8 GB (Q4_K_M · ~133 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gpt2 Large
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-large-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.5 GB free · 1K context
Download: gpt2-large.Q4_K_M.gguf0.55 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 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~1098 tok/s
- generation speed
- Cost per 1M output tokens
- $0.031
- per 1M output tokens
How Much VRAM Does Gpt2 Large Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.4 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: — context · ~1724 tok/s | |
| 0.4 GB | — | 3-bit small — 3-bit small quantization | Fits: — context · ~1680 tok/s | |
| 0.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: — context · ~1489 tok/s | |
| 0.5 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: — context · ~1456 tok/s | |
| 0.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: — context · ~1213 tok/s | |
| 0.6 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: — context · ~1024 tok/s | |
| 0.7 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: — context · ~885 tok/s | |
| 0.9 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: — context · ~736 tok/s |
Selected Q4_K_M
4-bit medium quantization0.5GBat 1K context
- Weights
- 0.1 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.5 GB free · ~1213 tok/s
≥50 tok/s needs ≥41 GB/s · RTX 5090 ~2157, RTX 3090 Ti ~1213
Which Hardware Can Run Gpt2 Large?
Q4_K_M · 0.5 GBGpt2 Large (Q4_K_M) requires 0.5 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 Large, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.5 GB free · up to — ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~915Simulate output on RTX 3080RTX 2080 Ti~742Simulate output on RTX 2080 TiRTX 3070 Ti~732Simulate output on RTX 3070 TiM3*8 GB~133Simulate output on iMac 24" M3 (8 GB)Orin Nano~123Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~88Simulate output on iPhone 1720 GB
fits · 19.5 GB free · up to — ctx
1 GPU
RX 7900 XT~889Simulate output on RX 7900 XT32–40 GB
fits · 31.5 GB free · up to — ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2157Simulate output on RTX 5090M5 Max36 GB~596Simulate output on Mac Studio M5 Max (36 GB)M532 GB~199Simulate output on MacBook Air 13" M5 (32 GB)Orin~247Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~163Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 0.8BAlibaba | — | 0.9 GB | ~753 | same size |
Specifications
- Publisher
- OpenAI
- Parameters
- 812M
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2022-03-02
- License
- MIT
Frequently Asked Questions
How much VRAM does Gpt2 Large need?
Gpt2 Large requires 0.5 GB of VRAM at Q4_K_M, or 1.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 812M × 4.8 bits ÷ 8 = 0.5 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 Large?
For Gpt2 Large, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gpt2 Large on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gpt2 Large. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gpt2 Large locally?
Yes — Gpt2 Large can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gpt2 Large?
At Q4_K_M, Gpt2 Large can reach ~8889 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1213 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 ÷ 0.54 × 0.65 = ~9630 tok/s
Estimated speed at Q4_K_M (0.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gpt2 Large?
At Q4_K_M, the download is about 0.49 GB. The full-precision BF16 version is 1.62 GB. The smallest option (IQ2_XXS) is 0.22 GB.
Which GPUs can run Gpt2 Large?
52 consumer GPUs can run Gpt2 Large 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 Large?
163 devices with unified memory can run Gpt2 Large at Q4_K_M (0.5 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.