May 19, 2026
Most teams I talk to that are struggling with LLM inference latency have already ruled out the obvious causes. The model is fine. The framework is configured correctly. The prompts are optimised. But performance is still inconsistent, and nobody can explain why.
The answer is almost always the same: shared infrastructure.
When you run LLM inference on a shared GPU cloud instance, you are not renting a GPU. You are renting access to a portion of one. The physical hardware, memory bandwidth, compute units, interconnect, is shared between tenants you do not know and cannot control.
This creates a specific class of problem that is difficult to debug because it is not deterministic. The same request, running the same model, at the same concurrency level, will produce different latency numbers at different times. Not because of anything in your stack, but because of what other tenants are doing on the same machine at that moment.
For batch workloads, this is manageable. You can schedule around it, retry jobs, absorb the variance. But for production inference serving, where you have real users waiting for real responses,latency variance is not a debugging problem. It is an infrastructure problem.
There are three main mechanisms:
Memory bandwidth contention. LLM inference is memory-bandwidth-bound, not compute-bound. The bottleneck is moving weights and KV cache between GPU memory and compute units — not the floating point operations themselves. When multiple tenants are running inference on the same GPU, they compete for that bandwidth. Your throughput degrades in proportion to how busy the other tenants are.
VRAM pressure. On a shared instance, your model runs in an allocated VRAM slot. If that slot is smaller than what your model needs for the KV cache at your target context length, the runtime starts offloading — which adds latency. On dedicated hardware, you have the full VRAM available. 96 GB per GPU on the RTX Pro 6000 Max-Q means you are not managing slot limits, you are managing your actual workload.
Virtualisation overhead. Most cloud GPU instances run on a hypervisor. That adds a layer of indirection between your workload and the hardware. For compute-heavy batch jobs, the overhead is small. For latency-sensitive inference, it is measurable.
Bare metal means direct access to the physical hardware. No virtualisation layer, no shared tenants, no competing jobs. The GPU you are allocated is the GPU you get, fully, every run.
For LLM inference, this matters in three specific ways:
Consistent latency. With no competing tenants on the hardware, every inference request runs on the same conditions. Latency is stable and predictable, which means you can plan around it, set expectations with your team, and build reliable products on top of it.
Full VRAM for your workload. On the 8x RTX Pro 6000 Blackwell Max-Q server, you have 96 GB GDDR7 ECC per GPU and 768 GB total across the full machine. You can run 70B parameter models in full FP16 precision without slot constraints, or larger models with FP4 quantisation, which the Blackwell architecture supports natively.
Framework compatibility without compromise. vLLM, TGI, TensorRT-LLM, Ollama, the major inference frameworks run directly on the hardware without hypervisor constraints. You configure for your model, not for the infrastructure.
I want to be honest about this: bare metal is not always the right answer.
If you are running occasional inference jobs, a few hundred requests per day, development and testing environments, batch workloads with flexible timing, shared GPU cloud is cheaper and flexible enough. The latency variance does not matter if nobody is waiting for the response in real time.
Bare metal becomes the right answer when you have at least one of these conditions:
At that point, the cost difference between shared and dedicated typically narrows significantly, and the performance difference is substantial.
Before moving to dedicated bare metal for inference, three things are worth verifying:
Model size and VRAM requirements. What is the minimum VRAM you need to run your model at your target context length and batch size? If that number exceeds 32 GB per GPU, you have already ruled out most shared GPU options.
Concurrency profile. How many concurrent inference requests do you need to serve? What does your p95 latency requirement look like? Run this against your current infrastructure and document the variance, it will tell you whether the problem is real.
Monthly cost at your actual usage hours. Shared GPU pricing looks cheap per hour. At 600+ hours per month of continuous usage, dedicated bare metal at $1.34/GPU/hr is frequently cheaper than the equivalent shared compute, and faster on every job.
LLM inference latency problems are infrastructure problems more often than they are model problems. Shared GPU cloud introduces variance that you cannot predict, cannot control, and cannot engineer around, because it originates outside your stack.
Dedicated bare metal removes that variable. You get the hardware, you get the full VRAM, and you get consistent performance from request 1 to request 10,000.
If your inference pipeline is in production and latency matters, it is worth running the numbers on what dedicated actually costs at your usage level. The answer is usually less than people expect.
1Legion provides dedicated bare metal GPU infrastructure for AI inference, rendering, and media production workloads. The 8x RTX Pro 6000 Blackwell Max-Q Bare Metal Server is available from $1.34/GPU/hr on a 12-month term, with no egress fees and direct engineering onboarding. Apply for a Bare Metal Pilot
