OLMoE from AI2 is the most accessible MoE on this list. 7 B total parameters means it fits on a 6 GB GPU at Q4, but only 1.3 B activate per token — so inference is fast even on modest hardware. Fully open: weights, training data, and recipes all released under Apache-2.0.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 3.924 GB | 4.42 GB | 4.92 GB | 85% |
| Q8_0 | 8 | 6.854 GB | 7.35 GB | 7.85 GB | 98% |
Context window & KV cache
Adds 0.50 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 4K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run OLMoE 1B-7B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
GUI. Browse → download → chat. MLX on Apple Silicon.
LM Studio home →- 1
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
bartowski/OLMoE-1B-7B-0924-Instruct-GGUF - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.
Community benchmarks
Real tokens/sec reports from people running OLMoE 1B-7B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host OLMoE 1B-7Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
5.6 GB
4.4 GB weights + 0.7 GB KV
Aggregate tok/s
192
across 1 user
Per-user tok/s
192
MoE active params
✅ Fits in 24 GB VRAM with 18.4 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run OLMoE 1B-7B?
OLMoE 1B-7B requires 4.42 GB VRAM minimum with Q4_K_M quantization. For full precision you need 7.35 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run OLMoE 1B-7B?
To run OLMoE 1B-7B, you need a GPU with at least 4.4 GB of VRAM for the smallest quantized version, up to 7.3 GB for the full model.
Is OLMoE 1B-7B good for coding?
OLMoE 1B-7B is versatile and can handle coding tasks well, though it may not be as specialized as models specifically trained for code generation.
OLMoE 1B-7B vs Llama 3.1 8B?
OLMoE 1B-7B has fewer parameters (6.9B) compared to Llama 3.1 8B, but it uses a more efficient MoE architecture, making it lighter and potentially faster in certain tasks.
Can I run OLMoE 1B-7B on a Mac?
Yes, you can run OLMoE 1B-7B on a Mac with an M1 or M2 chip, provided you have the necessary VRAM and system resources.
How much VRAM does OLMoE 1B-7B need?
The VRAM requirement for OLMoE 1B-7B ranges from 4.4 GB to 7.3 GB, depending on the quantization level used.
Is OLMoE 1B-7B censored?
OLMoE 1B-7B is not inherently censored, but its responses can be filtered or moderated using external tools to ensure appropriate content.
Is OLMoE 1B-7B commercial-use allowed?
Yes, OLMoE 1B-7B is licensed under Apache-2.0, which allows for commercial use without additional fees.
OLMoE 1B-7B context length?
OLMoE 1B-7B supports a context length of 4096 tokens, which is suitable for handling longer conversations and documents.
Does OLMoE 1B-7B support function calling?
OLMoE 1B-7B does not natively support function calling, but you can integrate it with external systems to achieve this functionality.
OLMoE 1B-7B quantization options?
OLMoE 1B-7B supports various quantization options, including 4-bit, 8-bit, and full precision, allowing you to balance between model size and performance.
Can OLMoE 1B-7B run on CPU?
While OLMoE 1B-7B can run on a CPU, it will be significantly slower compared to running on a GPU due to the model's size and complexity.
OLMoE 1B-7B fine-tuning?
OLMoE 1B-7B can be fine-tuned for specific tasks using frameworks like Hugging Face Transformers, but it requires substantial computational resources and data.
OLMoE 1B-7B system requirements?
To run OLMoE 1B-7B, you need a system with at least 16 GB of RAM, a modern CPU, and a GPU with 4.4 GB to 7.3 GB of VRAM, depending on the quantization level.
OLMoE 1B-7B performance benchmark?
Performance benchmarks for OLMoE 1B-7B vary, but it typically processes around 100-200 tokens per second on a high-end GPU, with lower speeds on less powerful hardware.
OLMoE 1B-7B for RAG?
OLMoE 1B-7B can be used for Retrieval-Augmented Generation (RAG), but you may need to integrate it with a retrieval system to fetch relevant documents.
OLMoE 1B-7B for agents?
OLMoE 1B-7B can be used to power conversational agents and chatbots, thanks to its ability to generate coherent and contextually relevant responses.
OLMoE 1B-7B for coding vs general?
OLMoE 1B-7B is generally capable in both coding and general tasks, but it may not perform as well as specialized models in either domain.
OLMoE 1B-7B vs ChatGPT?
OLMoE 1B-7B is smaller and more efficient than ChatGPT, but it may not match ChatGPT's performance in complex, multi-turn conversations.
OLMoE 1B-7B download size?
The download size for OLMoE 1B-7B varies based on quantization, ranging from approximately 2 GB for the 4-bit quantized version to 14 GB for the full precision model.
Best quant for OLMoE 1B-7B?
The best quantization for OLMoE 1B-7B depends on your hardware and performance needs. 8-bit quantization offers a good balance between model size and accuracy, while 4-bit is more lightweight but may sacrifice some performance.