LLM Infrastructure

Run Llama 70B+ at Full Precision

The only cloud with 512GB unified memory. Run Llama 70B, DeepSeek-R1 671B, and Llama 405B without quantization, without multi-GPU complexity, without compromise.

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The Memory Problem

Why 512GB Changes Everything

Large language models are memory-hungry. Here's what you can actually run on different infrastructure.

Model	Memory Required (FP16)	NVIDIA H100 (80GB)	MetalCloud (512GB)
Llama 7B	14GB	✓ Single GPU	✓
Llama 13B	26GB	✓ Single GPU	✓
Llama 70B	140-168GB	✗ Requires 2+ GPUs	✓ Single machine, 344GB spare
Llama 70B + 128K context	~207GB	✗ Requires 3+ GPUs	✓ Single machine
Llama 405B (INT4)	~220GB	✗ Requires 4+ GPUs	✓ Single machine
DeepSeek-R1 671B (INT4)	~350GB	✗ Requires 5+ GPUs	✓ Single machine

Multi-GPU setups require NVLink, tensor parallelism, and cost $6,000-$12,000+/month. MetalCloud: from £3.50/hour.

Simple API

Inference in 3 Lines of Code

No infrastructure to manage. No GPU drivers to configure. Just Python.

                Python
                pip install metalcloud
            

import metalcloud

# Run Llama 70B at full FP16 precision - impossible on single NVIDIA GPU
job = metalcloud.Job(
    model='meta-llama/Llama-3.3-70B',
    precision='fp16',           # Full precision, no quantization
    min_memory_gb=256,         # Request 256GB (70B needs ~168GB)
    max_price_per_hour=4.00    # Budget cap in GBP
)

# Submit inference with massive context window
result = job.inference(
    prompt="Analyze the complete works of Shakespeare and identify...",
    max_tokens=8000,
    context_window=100000      # 100K tokens - trivial with 512GB
)

print(result.text)
            

Use Cases

What You Can Build

Real-world applications that require massive memory and full precision.

Long-Context Applications

Process entire documents, codebases, or conversation histories without truncation.

Analyze 100+ page documents in a single request
Code review across entire repositories
Multi-turn conversations with full history
Legal document analysis and comparison

Full Precision Research

When quantization artifacts are unacceptable for your use case.

Academic research requiring reproducibility
Medical and scientific applications
Financial modeling and analysis
Quality benchmarking and evaluation

Massive Model Inference

Run frontier models that don't fit on traditional hardware.

Llama 405B for state-of-the-art performance
DeepSeek-R1 671B reasoning model
Custom fine-tuned large models
Ensemble inference pipelines

Production APIs

Deploy inference endpoints without multi-GPU complexity.

Simple scaling without tensor parallelism
Consistent latency without GPU coordination
Per-second billing for cost efficiency
Global edge deployment options

Why It Works

The Unified Memory Advantage

Apple Silicon's architecture fundamentally changes what's possible.

⚡

Zero Memory Copy

CPU and GPU share the same memory pool. No PCIe transfers, no bottlenecks, no wasted bandwidth moving data between pools.

⚡

800GB/s Bandwidth

Memory bandwidth exceeds discrete GPUs. Data flows to compute cores at maximum speed without interconnect overhead.

🔧

No Parallelism Overhead

No tensor sharding. No NVLink. No complex distributed inference. One model, one machine, full speed.

🌱

10x Power Efficiency

Entire Mac Studio draws under 100W. Equivalent NVIDIA setup requires 700W+ just for GPUs. Your carbon footprint, minimized.