RTX 5060 Ti 16GB for Llama 3 8B

Llama 3 8B (and its 3.1 / 3.2 / 3.3 refreshes) is the workhorse open LLM of 2026. On the RTX 5060 Ti 16GB at our dedicated GPU hosting it is a comfortable production fit – probably the most common deployment we ship.

Contents

• VRAM fit by precision
• Deployment config
• Performance
• Concurrency
• Variants and alternatives

VRAM Fit

FP8 is the sweet spot: good quality, comfortable KV cache, production-grade concurrency, Blackwell-native tensor cores.

Deployment

vLLM with FP8 checkpoint:

python -m vllm.entrypoints.openai.api_server \
  --model neuralmagic/Llama-3.1-8B-Instruct-FP8 \
  --quantization fp8 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.92 \
  --enable-prefix-caching \
  --served-model-name llama-3.1-8b

Tune further:

• --max-num-seqs 24 for 14-user concurrency target
• --max-num-batched-tokens 8192 for prefill efficiency
• --enable-chunked-prefill if mixing short chat with long RAG prompts
• --kv-cache-dtype fp8 to double KV cache capacity

Performance

Concurrency

Production SLA of 30+ t/s per user:

• Comfortable: 10-14 concurrent users
• Push: 16-18 concurrent (p99 TTFT grows)
• Breaks: 25+ concurrent (queue builds, KV evictions)

For higher concurrency run two 5060 Ti replicas data-parallel behind a load balancer (~28 concurrent) or step up to 5080.

Variants and Alternatives

• Llama 3.1 8B Instruct – general chat
• Llama 3.2 8B – slight refresh
• Hermes 3 8B – less restrictive fine-tune, better agent
• Llama 3 8B Code – if coding matters, see Qwen Coder 7B instead

See also: Llama 3 8B benchmark, monthly cost, FP8 Llama deployment.