GPT Neo 2.7B — Hardware Requirements & GPU Compatibility
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GPT Neo 2.7B is a 2.7B-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. At BF16 it needs about 5.98 GB of VRAM — see which GPUs and Macs can run it below.
20.2K downloads
504 likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 2.7B
- Architecture
- GPTNeoForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2022-03-02
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does GPT Neo 2.7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 6.0 GB | — | 5.44 GB | Brain floating point 16 — preferred for training |
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 GPT Neo 2.7B?
BF16 · 6.0 GBGPT Neo 2.7B (BF16) requires 6.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run GPT Neo 2.7B?
BF16 · 6.0 GB58 devices with unified memory can run GPT Neo 2.7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does GPT Neo 2.7B need?
GPT Neo 2.7B requires 6.0 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.7B × 16 bits ÷ 8 = 5.4 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF166.0 GB- Can I run GPT Neo 2.7B on a Mac?
GPT Neo 2.7B requires at least 6.0 GB at BF16, 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 GPT Neo 2.7B locally?
Yes — GPT Neo 2.7B can run locally on consumer hardware. At BF16 quantization it needs 6.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT Neo 2.7B?
At BF16, GPT Neo 2.7B can reach ~803 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~110 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 ÷ 6.0 × 0.65 = ~870 tok/s
Estimated speed at BF16 (6.0 GB)
~870 tok/s~110 tok/s~870 tok/s~803 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GPT Neo 2.7B?
At BF16, the download is about 5.44 GB.
- Which GPUs can run GPT Neo 2.7B?
52 consumer GPUs can run GPT Neo 2.7B at BF16 (6.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run GPT Neo 2.7B?
59 devices with unified memory can run GPT Neo 2.7B at BF16 (6.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.