Social listening is a volume problem. A mid-sized brand generates hundreds of thousands of daily mentions across X, Reddit, TikTok comments and review sites; every one needs sentiment, topic, entity, intent and urgency scores before it can reach a dashboard. The RTX 5060 Ti 16GB on UK dedicated GPU hosting (Blackwell GB206, 4,608 CUDA cores, 16 GB GDDR7, 180 W TDP) can run the full classification pipeline at millions of posts per day on a single card.
Contents
- Pipeline architecture
- Per-stage capacity
- Nuance layer with Llama
- Economics vs SaaS
- Operational notes
Pipeline architecture
Split the workload into a cheap fast lane and an expensive slow lane. DeBERTa-v3 handles 95% of posts at millisecond latencies; Llama 3.1 8B FP8 re-scores the ambiguous 5% that DeBERTa flags with confidence below 0.7. This hybrid keeps latency low and total cost flat.
- Ingest from X/Reddit/TikTok APIs into a Kafka topic.
- DeBERTa-v3-large fine-tuned for 3-class sentiment and 20-class topic.
- BGE-base embeddings (10,200 texts/s) for deduplication and clustering.
- Llama 3.1 8B FP8 nuance pass on low-confidence items (sarcasm, mixed sentiment).
- Alert rules on crisis signals (sudden negative spike, high-follower amplification).
Per-stage capacity
| Stage | Model | Throughput | Daily capacity (16 h) |
|---|---|---|---|
| Sentiment | DeBERTa-v3-large INT8 | 1,800 posts/s | 103M posts |
| Topic multi-label | DeBERTa-v3-base | 2,400 posts/s | 138M posts |
| Embedding dedup | BGE-base FP16 | 10,200 texts/s | 587M texts |
| Nuance LLM (5%) | Llama 3.1 8B FP8 | 720 t/s aggregate | ~2.4M posts |
The LLM stage is the bottleneck; reserve it for borderline cases using DeBERTa confidence gating.
Nuance layer with Llama
Llama 3.1 8B FP8 with a structured-output schema returns sarcasm score, sentiment, brand mention type and competitor comparison flag in a single 180-token generation. At 112 t/s batch-1 and continuous batching to 720 t/s aggregate (see our FP8 Llama deployment), you get roughly four million nuance calls per day at 50% utilisation – comfortably above the 5% escalation rate for 50M daily posts.
Economics vs SaaS
| Option | 10M posts/day | Data residency | Custom taxonomy |
|---|---|---|---|
| Brandwatch-class SaaS | £8,000-£25,000/mo | US/EU | Limited |
| OpenAI gpt-4o-mini per post | £12,000/mo | US | Full |
| Self-hosted 5060 Ti | Fixed monthly | UK | Full |
Operational notes
Queue incoming posts via Redis Streams, batch to 64 items before DeBERTa inference, and use Prometheus to track the confidence-gating ratio – if more than 15% of traffic escalates to the LLM, retrain DeBERTa on fresh hard negatives. Store raw embeddings for six months to power retrospective clustering and trend analysis via the embedding server.
Social listening at millions-per-day scale
DeBERTa plus Llama nuance on Blackwell 16GB. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: classification pipelines, content tagging, embedding throughput, reranker server.
