Performance
Bare Metal GPU Performance, Managed Simplicity
Get exclusive GPU access with zero virtualization overhead — without buying hardware, managing drivers, or dealing with datacenter logistics. VFIO passthrough delivers true bare-metal performance in a fully managed cloud.
TL;DR
- →VFIO passthrough = bare metal performance — exclusive GPU access with 0% virtualization overhead
- →No hardware management — no procurement, no rack space, no cooling, no driver updates
- →Instant deployment — go from zero to a running GPU workload in under 5 minutes
- →Auto-failover — if hardware fails, your workload migrates automatically in seconds
The Bare Metal Promise
When ML engineers say they want "bare metal," they mean three things: full GPU access without sharing, no virtualization overhead eating into performance, and maximum throughput for their workloads. The appeal is straightforward — you get the entire GPU, every CUDA core, every byte of VRAM, every bit of memory bandwidth.
For performance-sensitive workloads — model training, high-throughput inference, real-time AI applications — this matters. Even a 10% overhead at scale translates to thousands of dollars in wasted compute per year.
The Bare Metal Reality
The problem with bare metal is everything that comes with it. Getting bare-metal GPU performance has traditionally meant owning and operating actual hardware — and that's a full-time job.
Hardware Procurement
GPU servers have 3-6 month lead times. H100 systems are backordered well into the future. Even consumer GPUs like the RTX 4090 require sourcing, building, and testing complete server systems. Budget $15,000-$250,000+ per server depending on the GPU.
Rack Space, Power & Cooling
A single GPU server draws 1-4 kW of power. You need colocation space ($500-2,000/mo per rack), redundant power, and adequate cooling. GPU servers run hot — thermal throttling due to poor cooling directly reduces performance.
Driver Updates & Maintenance
NVIDIA drivers need regular updates for CUDA compatibility, security patches, and performance improvements. Each update risks breaking your workload and requires testing. Multiply by every server in your fleet.
Hardware Failures
GPUs fail. Power supplies fail. SSDs fail. Memory fails. When hardware goes down on bare metal, you're on your own — source replacement parts, schedule maintenance, and accept the downtime. No automatic failover.
Long Lead Times
Need to scale up? Ordering, shipping, racking, and configuring new hardware takes weeks to months. Your ML team is blocked while waiting for infrastructure to catch up to demand.
The total cost of ownership for bare metal goes far beyond the GPU price tag. When you factor in infrastructure, personnel, and downtime, many teams realize they're spending more on operations than on compute.
VectorLay: Bare Metal Performance, Cloud Simplicity
VectorLay solves the bare-metal dilemma with a technology called VFIO (Virtual Function I/O) GPU passthrough. Instead of virtualizing the GPU (which adds overhead) or sharing it between tenants (which adds contention), VFIO passes the physical GPU device directly to your isolated VM. The result: 100% of the GPU's performance with none of the hardware management.
Exclusive GPU Access via VFIO
The physical GPU is passed directly to your VM using IOMMU hardware support. Your workload sees the actual GPU device — not a virtual one. CUDA, cuDNN, and TensorRT work exactly as they would on bare metal because, from the GPU's perspective, it is bare metal.
Hardware-Level Isolation
Each workload runs in its own VM with dedicated CPU, memory, and GPU resources. This isn't container-level isolation — it's hardware-enforced by the CPU's IOMMU. No noisy neighbors, no shared kernel, no side-channel risks.
Zero Virtualization Overhead
Unlike vGPU or GPU partitioning solutions that sit between your workload and the hardware, VFIO passthrough has zero GPU overhead. Benchmarks show identical performance to bare metal across CUDA, inference, and training workloads.
Performance Comparison
Here's how VectorLay's VFIO passthrough stacks up against shared GPU cloud instances and actual bare-metal hardware:
| Metric | VectorLay (VFIO) | Shared GPU Cloud | Bare Metal |
|---|---|---|---|
| GPU access | Exclusive (full device) | Shared (time-sliced or MIG) | Exclusive (full device) |
| GPU overhead | 0% | 5-15% | 0% |
| Driver management | Managed by platform | Managed by platform | Manual |
| Failover | Automatic (<30s) | Varies by provider | None (manual repair) |
| Deploy time | Minutes | Minutes to hours | Weeks to months |
| Noisy neighbors | None (VM isolation) | Yes (shared GPU) | None (dedicated) |
VectorLay matches bare metal on every performance metric while eliminating the operational burden. Shared GPU clouds sacrifice performance for convenience — VectorLay gives you both.
Cost Comparison: Owning Bare Metal vs. VectorLay
The sticker price on a GPU server is just the beginning. Here's the true total cost of ownership for running a single RTX 4090 bare-metal server versus using VectorLay, calculated over one year:
| Cost Item | Bare Metal (1x RTX 4090 Server) | VectorLay (1x RTX 4090) |
|---|---|---|
| Server hardware (amortized/yr) | $5,000-8,000/yr | $0 |
| Colocation / rack space | $1,200-3,000/yr | $0 |
| Power & cooling | $1,500-3,000/yr | $0 (included) |
| Network / bandwidth | $600-1,200/yr | $0 (no egress fees) |
| Maintenance & repairs | $500-2,000/yr | $0 |
| GPU compute | $0 (owned hardware) | $4,234/yr ($0.49/hr 24/7) |
| Total annual cost | $8,800-17,200/yr | $4,234/yr |
And this comparison assumes you already have the hardware. Factor in the 3-6 month lead time to procure a GPU server, and VectorLay is delivering value from day one while your bare-metal order is still in the supply chain.
What Bare Metal Doesn't Include
When your bare-metal GPU fails, you're down until you physically replace it. VectorLay migrates your workload to a healthy node in under 30 seconds.
Scaling bare metal means buying more servers. On VectorLay, scale from 1 to 20 GPUs in minutes and scale back down when demand drops.
Bare metal means configuring firewalls, TLS, DNS, and load balancing yourself. VectorLay provides automatic HTTPS endpoints.
No on-call, no hardware maintenance, no driver updates. Your team focuses on ML, not infrastructure.
Use Cases Where Bare-Metal Performance Matters
Not every GPU workload needs bare-metal performance. But for these use cases, the difference between 0% overhead and 10% overhead is significant:
Model Training
Training runs that take days or weeks amplify any overhead. A 10% performance loss on a 7-day training run means 17 extra hours of compute time wasted. With VFIO passthrough, every CUDA core runs at full speed.
High-Throughput Inference
Serving hundreds of requests per second requires squeezing maximum throughput from every GPU. Virtualization overhead directly reduces your tokens/second and increases P99 latency. VFIO gives you the full memory bandwidth for batched inference.
Real-Time AI
Applications like live video processing, real-time speech synthesis, and interactive AI assistants are latency-sensitive. Even small overhead in the GPU pipeline translates to perceptible delays for end users.
For these workloads, the traditional choice was "accept virtualization overhead or manage your own hardware." VectorLay eliminates that trade-off entirely. You get the performance of bare metal with the operational simplicity of a managed cloud.
Get bare-metal performance without the bare-metal headaches
Deploy GPU workloads with exclusive hardware access, zero virtualization overhead, and automatic failover. No hardware to buy, no drivers to manage, no datacenter contracts. Just raw GPU performance on demand.