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Serving large language models to production users requires different hardware decisions than running models for individual experimentation. Production LLM inference has throughput requirements, concurrency targets, uptime SLAs, and VRAM demands that single workstations cannot satisfy. A purpose-built GPU server running vLLM on AMD EPYC with NVIDIA RTX PRO 6000 Blackwell GPUs is the standard architecture for on-premise LLM production serving in 2026.
What determines LLM inference server throughput
LLM inference throughput is determined by GPU memory bandwidth, total VRAM, and GPU count. Memory bandwidth drives tokens-per-second per GPU — LLM generation is memory-bandwidth-bound, so higher bandwidth means more tokens per second. More VRAM means more KV cache capacity for concurrent requests. More GPUs multiply throughput nearly linearly with tensor parallelism.
KV cache: the concurrency multiplier
On a 4-GPU server with 384GB VRAM, a 70B model at FP8 occupies approximately 70GB, leaving 314GB for KV cache. At a 32K context window, each concurrent request consumes approximately 1–2GB. This server handles 150–300 concurrent requests on a 70B model — well above the needs of most teams.
Recommended server configurations
| Team size | Target model | Server | Combined VRAM |
|---|---|---|---|
| 5–20 users | 7B–13B (FP16) | Single RTX PRO 6000 workstation | 96GB |
| 20–50 users | 70B (FP8) | 4-GPU EPYC server | 384GB |
| 50–200 users | 70B (FP16) | 8-GPU EPYC server | 768GB |
| Enterprise / 405B | 405B (FP8) | 8-GPU EPYC server | 768GB |
The ROI calculation
At consistent utilization, on-premise LLM inference infrastructure pays for itself within 4–8 months versus equivalent cloud GPU rental. Use the VRLA Tech AI ROI Calculator to run the exact numbers for your team — input your current monthly cloud or API spend and get a break-even date and 3-year TCO comparison.
From inference to training
Many teams start with an LLM inference server and later add training capacity. VRLA Tech’s AI training cluster configurations extend the same EPYC platform for distributed fine-tuning and training workloads alongside production inference. For organizations deploying at data center scale, see the VRLA Tech data center deployment page.
Browse servers on the VRLA Tech Server page. See the full AI deployment journey on the AI deployment stage overview.
Talk to a VRLA Tech engineer
Share your target model, concurrent user count, and current monthly AI spend. We calculate the exact break-even and configure the right server.
LLM inference servers. vLLM pre-validated. Ships configured.
3-year parts warranty. Lifetime US engineer support.
VRLA Tech has been building custom AI workstations and GPU servers since 2016. Customers include General Dynamics, Los Alamos National Laboratory, and Johns Hopkins University. All systems ship with a 3-year parts warranty and lifetime US-based engineer support.
