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.
2.1M downloads
353 likesSpecifications
- Publisher
- OpenAI
- Parameters
- 812M
- 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 Large Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.4 GB | — | 0.35 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.4 GB | — | 0.36 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.4 GB | — | 0.40 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.5 GB | — | 0.49 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.6 GB | — | 0.58 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.7 GB | — | 0.67 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.9 GB | — | 0.81 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 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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gpt2 Large?
Q4_K_M · 0.5 GB33 devices with unified memory can run Gpt2 Large, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Gpt2 Large
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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
VRAM usage by quantization
Q4_K_M0.5 GB- 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 IQ3_XS at 0.4 GB.
VRAM requirement by quantization
IQ3_XS0.4 GBIQ3_M0.4 GBIQ4_XS0.5 GBQ4_K_M ★0.5 GBQ5_K_M0.6 GBBF161.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gpt2 Large on a Mac?
Gpt2 Large requires at least 0.4 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 Large locally?
Yes — Gpt2 Large can run locally on consumer hardware. At Q4_K_M quantization it needs 0.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gpt2 Large?
At Q4_K_M, Gpt2 Large can reach ~5398 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 0.5 × 0.55 = ~5398 tok/s
Estimated speed at Q4_K_M (0.5 GB)
~5398 tok/s~1213 tok/s~4035 tok/s~3338 tok/sReal-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 (IQ3_XS) is 0.33 GB.
- Which GPUs can run Gpt2 Large?
35 consumer GPUs can run Gpt2 Large at Q4_K_M (0.5 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 Gpt2 Large?
33 devices with unified memory can run Gpt2 Large at Q4_K_M (0.5 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.