MetalCloud and Vast.ai are both distributed GPU marketplaces, but they offer fundamentally different hardware. MetalCloud specializes in Apple Silicon with massive unified memory; Vast.ai offers NVIDIA GPUs from individual hosts. Here's how to choose.
The Core Difference
Vast.ai is a marketplace for NVIDIA GPU compute—everything from consumer RTX 4090s to data center H100s. MetalCloud is exclusively Apple Silicon, offering a unique capability: up to 512GB of unified memory accessible to both CPU and GPU on a single machine.
This isn't about which is "better"—it's about which architecture fits your workload.
Quick Comparison
| Feature | MetalCloud | Vast.ai |
|---|---|---|
| Hardware | Apple Silicon (M1/M2/M3) | NVIDIA GPUs (RTX, A100, H100) |
| Max single-machine memory | 512GB unified | 80GB (H100) per GPU |
| Multi-GPU support | N/A (unified architecture) | Yes (NVLink, etc.) |
| CUDA support | No | Yes |
| MLX support | Native | No |
| PyTorch support | Yes (Metal backend) | Yes (CUDA backend) |
| Pricing model | Per-second, fixed rates | Auction/spot, variable |
| Price range | £0.40 - £3.50/hr | $0.10 - $5.00+/hr |
Memory Architecture: The Key Differentiator
The most important difference is memory architecture:
MetalCloud (Unified Memory): CPU and GPU share the same 512GB memory pool. No data transfer between CPU RAM and GPU VRAM. A 200GB model loads once and both CPU and GPU can access it instantly.
Vast.ai (Discrete VRAM): Each GPU has separate VRAM (up to 80GB on H100). Running a 200GB model requires either quantization, model parallelism across multiple GPUs, or CPU offloading with significant performance penalties.
Memory Comparison for Large Models
| Model | FP16 Memory | MetalCloud Solution | Vast.ai Solution |
|---|---|---|---|
| Llama 70B | ~140GB | 1× M3 Ultra (512GB) | 2× H100 (160GB total) |
| Llama 405B | ~810GB | 2× M3 Ultra (1TB total) | 11× H100 (880GB total) |
| DeepSeek-R1 671B | ~1.3TB | 3× M3 Ultra (1.5TB total) | 17× H100 (1.36TB total) |
Performance Characteristics
MetalCloud Strengths
- ✓ No memory transfer overhead
- ✓ Full precision without quantization
- ✓ Simpler deployment (no multi-GPU)
- ✓ Lower latency for memory-bound tasks
- ✓ Up to 10x more power efficient
Vast.ai Strengths
- ✓ Higher raw FLOPS (H100: 1979 TFLOPS)
- ✓ CUDA ecosystem compatibility
- ✓ Better for training workloads
- ✓ Higher memory bandwidth per GPU
- ✓ More GPU variety/price points
Use Case Analysis
Choose MetalCloud When:
- Running large models at full precision — No quantization trade-offs
- Memory is your bottleneck — 512GB on single machine
- Using MLX framework — Native Apple Silicon optimization
- Inference-heavy workloads — Optimized for serving
- Predictable pricing needed — Fixed rates, no auctions
- Power efficiency matters — 10x better perf/watt
Choose Vast.ai When:
- Training models — NVIDIA excels at training workloads
- CUDA-dependent code — Existing CUDA codebases
- Maximum raw throughput — H100 has higher FLOPS
- Budget flexibility — Spot pricing can be cheaper
- Specific NVIDIA features — Tensor Cores, NVLink
The Verdict
MetalCloud and Vast.ai serve different niches. For large model inference where memory capacity matters more than raw FLOPS, MetalCloud's 512GB unified memory is unmatched. For training workloads or CUDA-dependent pipelines, Vast.ai's NVIDIA marketplace offers more options. Many teams use both—Vast.ai for training, MetalCloud for memory-intensive inference.
Cost Comparison
Pricing varies significantly based on workload:
| Scenario | MetalCloud | Vast.ai |
|---|---|---|
| Llama 70B inference (1 hour) | £3.50 (1× M3 Ultra) | ~$6-10 (2× H100) |
| Small model fine-tuning | £1.20/hr (M3 Max) | $0.50-2/hr (RTX 4090) |
| Development/testing | £0.40/hr (M3 Pro) | $0.10-0.30/hr (spot) |
MetalCloud wins on memory-intensive inference; Vast.ai wins on budget training and development where spot pricing is available.