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.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 2K)Best on 24 GB (BF16 · ~110 tok/s · 2K)Macs from 16 GB (BF16 · ~23 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT Neo 2.7B
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
vllm serve EleutherAI/gpt-neo-2.7B --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (6 GB headroom) · BF16
- Generation speed
- ~99 tok/s
- generation speed
- Cost per 1M output tokens
- $0.34
- per 1M output tokens
How Much VRAM Does GPT Neo 2.7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.0 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~110 tok/s |
Selected BF16
Brain floating point 166.0GBat 2K context
- Weights
- 5.4 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.0 GB free · ~110 tok/s
≥50 tok/s needs ≥460 GB/s · RTX 5090 ~195, RTX 3090 Ti ~110
Which Hardware 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. 151 devices with unified memory can run GPT Neo 2.7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 2.0 GB free · up to 2K ctx
16 GPUs · 1 device family
RTX 3080~83Simulate output on RTX 3080RTX 2080 Ti~67Simulate output on RTX 2080 TiRTX 3070 Ti~66Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 14.0 GB free · up to 2K ctx
1 GPU
RX 7900 XT~80Simulate output on RX 7900 XT32–40 GB
fits · 26.0 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~195Simulate output on RTX 5090M5 Max36 GB~54Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~22Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
How good is GPT Neo 2.7B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- HellaSwag
Epoch AI · Mar 2021Lab-reported
Lab-reported42.7%
#45/47 open
- BoolQ
Epoch AI · Mar 2021Lab-reported
Lab-reported61.8%
#35/38 open
- OpenBookQA
Epoch AI · Mar 2021Lab-reported
Lab-reported23.2%
#25/26 open
- PIQA
Epoch AI · Mar 2021Lab-reported
Lab-reported72.9%
#37/40 open
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| Granite 3.1 2B InstructIBM | — | 3.2 GB | ~329 | same size |
| Gemma 2 2B ITGoogle | — | 2.5 GB | ~379 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| Nemotron Flash 3BNVIDIA | — | 7.6 GB | ~109 | same size |
| LFM2 2.6B LongevityLiquid AI | — | 2.3 GB | ~333 | same size |
Specifications
- Publisher
- EleutherAI
- Parameters
- 2.7B
- Architecture
- GPTNeoForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2022-03-02
- License
- MIT
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)
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
Can I run GPT Neo 2.7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run GPT Neo 2.7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 6.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run GPT Neo 2.7B locally?
Yes — GPT Neo 2.7B can run locally on consumer hardware. 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 ÷ 5.98 × 0.65 = ~870 tok/s
Estimated speed at BF16 (6.0 GB)
Real-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. 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?
151 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, 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.