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 Q4_K_M it needs about 8.71 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q3_K_M · 4K)Best on 24 GB (Q4_K_M · ~428 tok/s · 4K)Macs from 16 GB (Q4_K_M · ~100 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Emo 1b14b 1T
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve allenai/Emo_1b14b_1T --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (3 GB headroom) · Q4_K_M
- Generation speed
- ~201 tok/s
- generation speed
- Cost per 1M output tokens
- $0.11
- per 1M output tokens
How Much VRAM Does Emo 1b14b 1T Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~515 tok/s | |
| 7.2 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~480 tok/s | |
| 8.7 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~428 tok/s | |
| 10.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~385 tok/s | |
| 11.8 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~351 tok/s | |
| 14.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~308 tok/s | |
| 27.7 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 27.7 GB |
Selected Q4_K_M
4-bit medium quantization9.0GBat 4K context
- Weights
- 8.1 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 15.0 GB free · ~428 tok/s
≥50 tok/s needs ≥77 GB/s · RTX 5090 ~588, RTX 3090 Ti ~428
Which Hardware Can Run Emo 1b14b 1T?
Q4_K_M · 8.7 GBEmo 1b14b 1T (Q4_K_M) requires 8.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 4K context window can add up to 0.3 GB, bringing total usage to 9.0 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Emo 1b14b 1T, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 3.0 GB free · up to 4K ctx
10 GPUs
RTX 3080 Ti~401Simulate output on RTX 3080 TiRTX 5070~326Simulate output on RTX 5070RTX 4070~263Simulate output on RTX 407020 GB
fits · 11.0 GB free · up to 4K ctx
1 GPU
RX 7900 XT~193Simulate output on RX 7900 XT32–40 GB
fits · 23.0 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~588Simulate output on RTX 5090M5 Max36 GB~162Simulate output on Mac Studio M5 Max (36 GB)M532 GB~83Simulate output on MacBook Air 13" M5 (32 GB)Orin~124Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~80Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Qwen1.5 14BAlibaba | — | 22.2 GB | ~63 | same size |
| Baichuan2 13B Basebaichuan-inc | — | 11.5 GB | ~76 | same size |
| Llama 2 13B Chat HFMeta | — | 11.5 GB | ~76 | same size |
| Vicuna 13B V1.3LMSYS | — | 8.6 GB | ~76 | same size |
| Vllm Translategemma 12B ITchbae624 | — | 9.8 GB | ~73 | same size |
Specifications
- Publisher
- Allen AI
- Parameters
- 13.6B
- Active parameters
- 1.5B per token
- Architecture
- EmoForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2026-04-29
- License
- Other
Frequently Asked Questions
How much VRAM does Emo 1b14b 1T need?
Emo 1b14b 1T requires 8.7 GB of VRAM at Q4_K_M, or 27.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 13.6B × 4.8 bits ÷ 8 = 8.1 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.
KV Cache + Overhead ≈ 0.9 GB (at full 4K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Emo 1b14b 1T?
Yes, at Q8_0 (14.1 GB) or lower. Higher quantizations like BF16 (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Emo 1b14b 1T?
For Emo 1b14b 1T, Q4_K_M (8.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Emo 1b14b 1T on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Emo 1b14b 1T. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 8.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Emo 1b14b 1T locally?
Yes — Emo 1b14b 1T can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Emo 1b14b 1T?
At Q4_K_M, Emo 1b14b 1T can reach ~294 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~428 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 1.0 GB active ÷ (8000 × 0.65) = 0.184 ms, plus 16 layers × 0.055 ms = 0.880 ms, so 1000 ÷ 1.064 ms = ~940 tok/s
Estimated speed at Q4_K_M (8.7 GB)
Real-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 Q4_K_M, the download is about 8.14 GB. The full-precision BF16 version is 27.14 GB. The smallest option (Q2_K) is 5.77 GB.
Which GPUs can run Emo 1b14b 1T?
40 consumer GPUs can run Emo 1b14b 1T at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Emo 1b14b 1T?
150 devices with unified memory can run Emo 1b14b 1T at Q4_K_M (8.7 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.