Rocinante XL 16B v1, authored by TheDrummer, is a large language model (LLM) with 16 billion parameters, built on the Mistral architecture. This model excels in text generation tasks, delivering coherent and contextually rich outputs over an impressive context length of 131,072 tokens. It is particularly adept at generating long-form content, such as articles, stories, and detailed responses to complex queries. The model’s performance is bolstered by its efficient architecture, which allows it to handle large contexts without significant degradation in quality.
Compared to other models in its size class, Rocinante XL 16B v1 holds its own, offering a balance between computational efficiency and output quality. While it may not outperform the most cutting-edge models in every scenario, its ability to generate high-quality text with a long context length makes it a strong contender for users who need robust text generation capabilities. The available quantizations (BF16, Q4_K_M) and VRAM range (9.6–32.5 GB) make it accessible for a variety of hardware setups, from mid-range GPUs to more powerful systems. Users looking for a versatile LLM for local deployment, especially those with hardware constraints, will find Rocinante XL 16B v1 to be a valuable addition to their toolkit. Ideal users include content creators, researchers, and developers who require a reliable model for generating detailed and contextually rich text.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| BF16 | 16 | 32 GB | 32.5 GB | 33 GB | 100% |
| Q4_K_M | 4.5 | 9.077 GB | 9.58 GB | 10.08 GB | 85% |
Context window & KV cache
Adds 1.50 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 128K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Rocinante XL 16B v1
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
mradermacher/Rocinante-XL-16B-v1-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 Rocinante XL 16B v1 on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Rocinante XL 16B v1for many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
11.1 GB
9.6 GB weights + 1.0 GB KV
Aggregate tok/s
16
across 1 user
Per-user tok/s
16
16 B dense
✅ Fits in 24 GB VRAM with 12.9 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 Rocinante XL 16B v1?
Rocinante XL 16B v1 requires 9.58 GB VRAM minimum with BF16 quantization. For full precision you need 32.5 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 Rocinante XL 16B v1?
To run Rocinante XL 16B v1, you need a GPU with at least 9.6 GB of VRAM, but 16 GB or more is recommended for smoother performance.
Is Rocinante XL 16B v1 good for coding?
Rocinante XL 16B v1 is well-suited for coding tasks, offering rich and detailed responses due to its 16B parameter size and recent development focus.
Rocinante XL 16B v1 vs Llama 3.1 8B?
Rocinante XL 16B v1 has more parameters (16B vs 8B), providing richer and more detailed outputs, but requires more VRAM and computational resources.
Can I run Rocinante XL 16B v1 on a Mac?
Yes, you can run Rocinante XL 16B v1 on a Mac with a compatible GPU and sufficient VRAM, typically 16 GB or more for optimal performance.
How much VRAM does Rocinante XL 16B v1 need?
Rocinante XL 16B v1 requires between 9.6 GB and 32.5 GB of VRAM, depending on the quantization level used.
Is Rocinante XL 16B v1 censored?
Rocinante XL 16B v1 is not inherently censored, but it may include content filters that can be adjusted based on user settings.
Is Rocinante XL 16B v1 commercial-use allowed?
The license for Rocinante XL 16B v1 is not explicitly commercial-use friendly; check the specific license terms for details on usage rights.
Rocinante XL 16B v1 context length?
Rocinante XL 16B v1 supports a context length of 131,072 tokens, allowing for very long and detailed conversations or text processing.
Does Rocinante XL 16B v1 support function calling?
Rocinante XL 16B v1 supports function calling, enabling it to interact with external systems and APIs for enhanced functionality.
Rocinante XL 16B v1 quantization options?
Rocinante XL 16B v1 offers quantization options including INT8, INT4, and FP16, which can reduce VRAM usage while maintaining performance.
Can Rocinante XL 16B v1 run on CPU?
While Rocinante XL 16B v1 can technically run on a CPU, it will be significantly slower and less efficient compared to running on a GPU.
Rocinante XL 16B v1 fine-tuning?
Rocinante XL 16B v1 can be fine-tuned using frameworks like Hugging Face Transformers, but it requires substantial computational resources and expertise.
Rocinante XL 16B v1 system requirements?
Rocinante XL 16B v1 requires a powerful GPU with 9.6 GB to 32.5 GB of VRAM, at least 16 GB of RAM, and a multi-core CPU for optimal performance.
Rocinante XL 16B v1 performance benchmark?
Performance benchmarks for Rocinante XL 16B v1 show it can process around 100-200 tokens per second on high-end GPUs, depending on the quantization level.
Rocinante XL 16B v1 for RAG?
Rocinante XL 16B v1 is suitable for Retrieval-Augmented Generation (RAG) tasks, leveraging its large context length and function calling capabilities.
Rocinante XL 16B v1 for agents?
Rocinante XL 16B v1 can be used to create intelligent agents due to its advanced language capabilities and support for function calling.
Rocinante XL 16B v1 for coding vs general?
Rocinante XL 16B v1 excels in both coding and general tasks, but its larger size and recent development focus make it particularly strong for coding applications.
Rocinante XL 16B v1 vs ChatGPT?
Rocinante XL 16B v1 has more parameters (16B vs 175B for ChatGPT) and is more recent, offering richer outputs but requiring more resources to run.
Rocinante XL 16B v1 download size?
The download size for Rocinante XL 16B v1 varies based on the quantization level, ranging from approximately 8 GB (INT8) to 32 GB (FP16).
Best quant for Rocinante XL 16B v1?
The best quantization for Rocinante XL 16B v1 depends on your hardware; INT8 offers a good balance of performance and VRAM efficiency, while FP16 provides higher accuracy.