Can I run Magnum v4 72B on my device?

Magnum v4 72B requires a minimum of 43.7GB VRAM. Use RunThisModel to check your specific hardware compatibility and find the best quantization for your device.

How much VRAM does Magnum v4 72B need?

Magnum v4 72B needs 43.7GB VRAM at minimum (BF16 quantization). Higher quality quantizations need more: BF16: 144.5GB, Q4_K_M: 43.7GB.

How do I download Magnum v4 72B?

You can download Magnum v4 72B in GGUF format from HuggingFace (43.2GB minimum). Use the RunThisModel iOS app to download and run it directly on your device, or download manually from HuggingFace.

Can Magnum v4 72B run on iPhone?

Magnum v4 72B at 72B parameters is too large for most iPhones. Consider using an iPad with M-series chip or Mac with Apple Silicon.

Anthracite

Magnum v4 72B

Name: Magnum v4 72B
Author: Anthracite

Qwen2.5-72B fine-tuned on Claude-Opus-style literary data. Highest-quality long-form prose at the 72B class. Apache 2.0.

72B parametersqwen2apache-2.0128K context43.7GB - 144.5GB VRAM

Check Your Hardware

See which quantizations of Magnum v4 72B your hardware can run.

Quantization Options

Quantization	Bits	File Size	VRAM Needed	RAM Needed	Quality
BF16	16	144 GB	144.5 GB	145 GB	100%
Q4_K_M	4.5	43.2 GB	43.7 GB	44.2 GB	85%

Context window & KV cache

Adds 2.50 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 Magnum v4 72B

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/magnum-v4-72b-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 Magnum v4 72B on actual hardware.

No community runs yet for this model. Be the first to submit your numbers.

Self-host serving plan

Want to host Magnum v4 72Bfor 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

46.3 GB

43.7 GB weights + 2.1 GB KV

Aggregate tok/s

across 1 user

Per-user tok/s

72 B dense

⚠ Will spill 22.3 GB of weights to system RAM (~5× slower per offloaded layer). Use llama.cpp’s --cpu-offload-gb or vLLM’s --swap-space.

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 Magnum v4 72B?

Magnum v4 72B requires 43.7GB VRAM minimum with BF16 quantization. For full precision, you need 144.5GB VRAM.

What is the best quantization for Magnum v4 72B?

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