Solar 10.7B by Upstage is a large language model (LLM) with 10.7 billion parameters, built on the LLaMA architecture. It excels in text generation tasks, offering coherent and contextually relevant responses over a context length of 4096 tokens. This makes it suitable for a wide range of applications, from content creation and chatbot interactions to summarization and translation. The model is licensed under Apache-2.0, making it accessible for both commercial and non-commercial projects. With over 47,000 downloads and 649 likes, it has gained significant traction in the community.
In its size class, Solar 10.7B holds its own, delivering performance that is competitive with other models of similar parameter count. It is efficient in terms of VRAM usage, requiring between 6.5 and 11.1 GB, which is manageable for many modern GPUs. The availability of quantized versions (Q4_K_M and Q8_0) further enhances its efficiency, making it a practical choice for users with more modest hardware. Ideal users include developers, researchers, and businesses looking to deploy a powerful yet resource-efficient LLM locally. While it can run on a variety of hardware, a GPU with at least 8 GB of VRAM is recommended for optimal performance.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 6.018 GB | 6.52 GB | 7.02 GB | 85% |
| Q8_0 | 8 | 10.621 GB | 11.12 GB | 11.62 GB | 98% |
Context window & KV cache
Adds 0.63 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 Solar 10.7B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull solar - 2
Chat
ollama run solar - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"solar","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Solar 10.7B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Solar 10.7Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
7.8 GB
6.5 GB weights + 0.8 GB KV
Aggregate tok/s
23
across 1 user
Per-user tok/s
23
10.7 B dense
✅ Fits in 24 GB VRAM with 16.2 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 Solar 10.7B?
Solar 10.7B requires 6.52 GB VRAM minimum with Q4_K_M quantization. For full precision you need 11.12 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 Solar 10.7B?
To run Solar 10.7B, you need a GPU with at least 6.5 GB of VRAM, though 11.1 GB is recommended for optimal performance, especially with higher quantization levels.
Is Solar 10.7B good for coding?
Yes, Solar 10.7B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 4096 tokens.
Solar 10.7B vs Llama 3.1 8B?
Solar 10.7B has more parameters (10.7B vs 8B) and a longer context length (4096 vs 2048), which can result in better performance on complex tasks but requires more VRAM.
Can I run Solar 10.7B on a Mac?
Yes, you can run Solar 10.7B on a Mac, but ensure your Mac has a compatible GPU with at least 6.5 GB of VRAM.
How much VRAM does Solar 10.7B need?
Solar 10.7B requires between 6.5 GB and 11.1 GB of VRAM, depending on the quantization level used.
Is Solar 10.7B censored?
Solar 10.7B is not inherently censored, but it adheres to community guidelines and ethical standards set by the model's creators.
Is Solar 10.7B commercial-use allowed?
Yes, Solar 10.7B is licensed under the Apache-2.0 license, which allows for commercial use as long as you comply with the terms of the license.
Solar 10.7B context length?
The context length for Solar 10.7B is 4096 tokens, allowing for longer and more complex inputs and outputs.
Does Solar 10.7B support function calling?
Solar 10.7B supports function calling, enabling it to interact with external systems and APIs effectively.
Solar 10.7B quantization options?
Solar 10.7B supports various quantization options, including 4-bit, 8-bit, and 16-bit, which can reduce VRAM usage and improve inference speed.
Can Solar 10.7B run on CPU?
While Solar 10.7B can run on a CPU, it will be significantly slower compared to running on a GPU, especially for larger models and longer sequences.
Solar 10.7B fine-tuning?
Solar 10.7B can be fine-tuned on custom datasets to improve performance on specific tasks, but this requires significant computational resources and expertise.
Solar 10.7B system requirements?
To run Solar 10.7B, you need a system with a GPU supporting at least 6.5 GB of VRAM, 32 GB of RAM, and a multi-core CPU. A solid-state drive (SSD) is recommended for faster data loading.
Solar 10.7B performance benchmark?
Performance benchmarks for Solar 10.7B vary, but it typically processes around 100-200 tokens per second on a high-end GPU like an RTX 3090, with lower throughput on less powerful GPUs.
Solar 10.7B for RAG?
Solar 10.7B can be used for Retrieval-Augmented Generation (RAG) tasks, leveraging its strong reasoning and context handling to integrate retrieved information effectively.
Solar 10.7B for agents?
Solar 10.7B is suitable for creating conversational agents and chatbots, thanks to its large context length and ability to handle complex dialogues.
Solar 10.7B for coding vs general?
Solar 10.7B performs well in both coding and general tasks, but its strength in reasoning makes it particularly effective for coding and technical applications.
Solar 10.7B vs ChatGPT?
Solar 10.7B offers a larger context length (4096 vs 2048) and more parameters (10.7B vs 175B for GPT-3), making it more suitable for local deployment and resource-constrained environments.
Solar 10.7B download size?
The download size for Solar 10.7B varies depending on the quantization level, ranging from approximately 5 GB for 4-bit quantization to 22 GB for full precision.
Best quant for Solar 10.7B?
The best quantization for Solar 10.7B depends on your hardware and use case. 8-bit quantization offers a good balance between performance and VRAM efficiency, while 4-bit is ideal for systems with limited VRAM.