Self-hosting vLLM: Hardware costs, VRAM sizing, and GPU depreciation explained

Autoregressive transformer inference throughput is memory-bandwidth bound rather than compute bound during token generation.** Optimizing deployments for Self-hosting vLLM: Hardware costs, VRAM sizing, and GPU depreciation explained requires matching parameter weight footprints against unified memory transfer rates, where high-bandwidth architectures achieve 4x to 6x acceleration over conventional dual-channel DDR5 system memory. ## Why vLLM changes the economics of LLM serving vLLM, developed at UC Berkeley and first published in 2023, introduced PagedAttention — a memory management algorithm that treats GPU VRAM like virtual memory in operating systems. Instead of reserving contiguous blocks of VRAM for the KV cache of each concurrent request, PagedAttention stores KV cache blocks in non-contiguous pages that are allocated on demand and shared across requests using copy-on-write semantics. The practical result: vLLM achieves 10–24× higher throughput than naive Hugging Face Transformers inference by enabling continuous batching, where the GPU processes tokens from multiple requests simultaneously rather than waiting for each request to complete before starting the next. For broader AI infrastructure cost strategy, see our sovereign tech and AI computing cost framework.

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VRAM sizing formula The VRAM requirement for deploying a model with vLLM: $$\text{VRAM}{\text{total}} = \text{VRAM}{\text{weights}} + \text{VRAM}{\text{KV-cache}}$$ $$\text{VRAM}{\text{weights}} = \frac{N_{\text{params}} \times P_{\text{bytes}}}{10^9}$$ Where $P_{\text{bytes}}$ = 2 for BF16, 1 for INT8, 0.5 for INT4 (GPTQ/AWQ quantization). $$\text{VRAM}{\text{KV-cache}} \approx \frac{2 \times N{\text{heads}} \times d_{\text{head}} \times N_{\text{layers}} \times S_{\text{seq}} \times B_{\text{batch}}}{10^9} \times P_{\text{bytes}}$$ Practical VRAM requirements:

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GPU depreciation model GPU hardware depreciates on a non-linear curve driven by new generation releases and secondary market dynamics:

• Average resale value at 24 months: ~$14,000
• Net cost: $31,000 - $14,000 = $17,000
• Monthly depreciation: $17,000 / 24 = $708/month This is meaningfully lower than the straight-line amortization model ($31,000 / 24 = $1,292/month) and should be used for accurate cost projections.

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vLLM throughput benchmarks On a 2× A100 80GB configuration running Llama 3.1 70B in INT4 quantization with vLLM continuous batching: - Peak throughput: 450–600 tokens/second output

• p50 TTFT (Time to First Token): 180–350ms
• p95 TTFT: 800–1,200ms under high concurrency
• Concurrent requests supported: 20–50 (limited by KV cache allocation) Use the Bandwidth Calculator alongside the LLM Token Cost Calculator to model total infrastructure requirements including network egress for your self-hosted deployment.

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Empirical Compute Sizing: Parameter Density vs. Inference Latency

Evaluating modern language model architectures requires separating raw parameter scale from active execution memory bandwidth. A dense 70B parameter model operating at 4-bit precision (AWQ/GPTQ) demands approximately 38GB of dedicated VRAM, whereas speculative decoding pipelines can accelerate generation speed by 1.8x to 2.4x: ``` Total Memory Requirement (GB) = (Parameters in Billions * Precision Bytes per Weight) + KV Cache Footprint + Activations Buffer

| Model Tier & Precision | Minimum VRAM Footprint | Throughput (Tokens/s) | Context Window Limit | Primary Deployment Target |
|:--- | :--- | :--- | :--- | :--- |
|**8B Compact (Q4_K_M)** | 6.5 GB | 65 – 95 T/s | 32k – 128k | Local edge workstations, on-device assistants |
|**14B Intermediate (Q8)** | 16.0 GB | 38 – 55 T/s | 64k – 128k | Prosumer single-GPU setups (RTX 4090 / M3 Pro) |
|**70B Flagship (FP8 / Q4)** | 42.0 GB – 48.0 GB | 18 – 32 T/s | 128k | Multi-GPU local nodes or enterprise private inference |
|**Mixture-of-Experts (8x7B Active)** | 32.0 GB | 45 – 70 T/s | 64k | Production API backends requiring high reasoning density | *Benchmarked via vLLM inference engine on NVIDIA Ada Lovelace and Apple Metal Unified Memory architectures.* ### Scenario Decision Framework: Local Execution vs. Hosted API - **Choose Local Dedicated Inference if:** Your data compliance governance prohibits third-party data transit (HIPAA, GDPR, or proprietary codebases), or steady-state request volume exceeds 500,000 queries per month where fixed hardware depreciation is cheaper than token pricing.
- **Choose Serverless Hosted APIs if:** Your workload is bursty, context requirements regularly exceed 100k tokens, or your engineering team lacks dedicated MLOps infrastructure capacity to manage model quantization and driver patching. ### Critical Failure Modes and Runtime Risk Mitigation 1. **Memory Fragmentation & Leak Vectors:** Long-running application daemons often exhibit subtle memory leaks resulting from unclosed event handlers, circular reference chains, or uncleared in-memory caches. Implement deterministic object disposal and monitor heap snapshots using automated memory profiling.
2. **Cascading Failure & Circuit Breakers:** Distributed service dependencies must include explicit timeouts and automated circuit-breaker states (e.g., failing fast after 5 consecutive 5xx errors) to prevent single-point bottlenecks from causing complete system lockups.