Emo 1b14b 1T — Hardware Requirements & GPU Compatibility
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Emo 1b14b 1T is a 13.6B-parameter open language model from Allen AI. It supports a context window of up to 4,096 tokens. At BF16 it needs about 27.71 GB of VRAM — see which GPUs and Macs can run it below.
2.1K downloads
25 likesSpecifications
- Publisher
- Allen AI
- Parameters
- 13.6B
- Architecture
- EmoForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2026-05-08
- License
- Other
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HuggingFace
How Much VRAM Does Emo 1b14b 1T Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 27.7 GB | 28.0 GB | 27.14 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Emo 1b14b 1T?
BF16 · 27.7 GBEmo 1b14b 1T (BF16) requires 27.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 37+ GB is recommended. Using the full 4K context window can add up to 0.3 GB, bringing total usage to 28.0 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Emo 1b14b 1T?
BF16 · 27.7 GB15 devices with unified memory can run Emo 1b14b 1T, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Emo 1b14b 1T need?
Emo 1b14b 1T requires 27.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 13.6B × 16 bits ÷ 8 = 27.1 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.9 GB (at full 4K context)
VRAM usage by quantization
BF1627.7 GBBF16 + full context28.0 GB- Can I run Emo 1b14b 1T on a Mac?
Emo 1b14b 1T requires at least 27.7 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 Emo 1b14b 1T locally?
Yes — Emo 1b14b 1T can run locally on consumer hardware. At BF16 quantization it needs 27.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Emo 1b14b 1T?
At BF16, Emo 1b14b 1T can reach ~105 tok/s on AMD Instinct MI300X. 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 ÷ 27.7 × 0.55 = ~105 tok/s
Estimated speed at BF16 (27.7 GB)
~105 tok/s~79 tok/s~65 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Emo 1b14b 1T?
At BF16, the download is about 27.14 GB.
- Which GPUs can run Emo 1b14b 1T?
1 consumer GPU can run Emo 1b14b 1T at BF16 (27.7 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Emo 1b14b 1T?
15 devices with unified memory can run Emo 1b14b 1T at BF16 (27.7 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.