Can I run Rocinante XL 16B v1 on my device?

Rocinante XL 16B v1 requires a minimum of 10.1GB VRAM. Use RunThisModel to check your specific hardware compatibility and find the best quantization for your device.

How much VRAM does Rocinante XL 16B v1 need?

Rocinante XL 16B v1 needs 10.1GB VRAM at minimum (BF16 quantization). Higher quality quantizations need more: BF16: 32.5GB, Q4_K_M: 10.1GB.

How do I download Rocinante XL 16B v1?

You can download Rocinante XL 16B v1 in GGUF format from HuggingFace (9.6GB minimum). Use the RunThisModel iOS app to download and run it directly on your device, or download manually from HuggingFace.

Can Rocinante XL 16B v1 run on iPhone?

Rocinante XL 16B v1 at 16B parameters is too large for most iPhones. Consider using an iPad with M-series chip or Mac with Apple Silicon.

TheDrummer

Rocinante XL 16B v1

Name: Rocinante XL 16B v1
Author: TheDrummer

Newest Rocinante release — 16B upscaled Mistral-Nemo for richer prose at the 12-16GB tier. Recent (2026) release, smaller community footprint but actively developed.

16B parametersmistralother128K context10.1GB - 32.5GB VRAM

Check Your Hardware

See which quantizations of Rocinante XL 16B v1 your hardware can run.

Quantization Options

Quantization	Bits	File Size	VRAM Needed	RAM Needed	Quality
BF16	16	32 GB	32.5 GB	33 GB	100%
Q4_K_M	4.5	9.6 GB	10.1 GB	10.6 GB	85%

Context window & KV cache

Adds 1.50 GB to VRAM

Long 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.

Concurrent users: 1VRAM budget: 24 GBSystem RAM: 64 GBAllow NVMe spilloverPermit weights to spill to NVMe SSD (last resort, ~50× slower)

VRAM needed

11.6 GB

10.1 GB weights + 1.0 GB KV

Aggregate tok/s

across 1 user

Per-user tok/s

16 B dense

✅ Fits in 24 GB VRAM with 12.4 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.

Download & Run

HuggingFace

View model & download weights

Ollama

One-command install & run

See It In Action

Real model outputs generated via RunThisModel.com — watch responses stream in real time.

Llama 3.3 70B responding...

Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.

Frequently Asked Questions

How much VRAM do I need to run Rocinante XL 16B v1?

Rocinante XL 16B v1 requires 10.1GB VRAM minimum with BF16 quantization. For full precision, you need 32.5GB VRAM.

What is the best quantization for Rocinante XL 16B v1?

Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.