Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs | Signal Canvas | ScienceToStartup

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Display score: 8.7/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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Evidence Receipt

Route status: degraded

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: stale

Source paper: Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

PDF: https://arxiv.org/pdf/2601.09527v1.pdf

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T21:31:49.672Z

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Subject: Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

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Evidencepartial
The RTX 5090 delivers 3.5-4.6x higher throughput than the 5060 Ti
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Self-hosted inference costs $0.001-0.04 per million tokens (electricity only)
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which is 40-200x cheaper than budget-tier cloud APIs
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NVFP4 quantization provides 1.6x throughput over BF16 with 41% energy reduction and only 2-4% quality loss
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with hardware breaking even in under four months at moderate volume (30M tokens/day)
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Our results show that consumer GPUs can reliably replace cloud inference for most SME workloads, except latency-critical long-context RAG
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budget GPUs achieve the highest throughput-per-dollar for API workloads with sub-second latency
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with 21x lower latency for RAG
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Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

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