RTX 5060 Ti 16GB for DeepSeek-R1-Distill

DeepSeek-R1 is the open-weight reasoning breakthrough of the last cycle, and the distilled series makes that reasoning capability tractable on single-GPU hardware. On a Blackwell RTX 5060 Ti 16GB you can run DeepSeek-R1-Distill-Qwen-7B at 115 tokens/s FP8 and Distill-Llama-8B at 108 t/s, with reasoning scores that beat GPT-4o on MATH-500. This post covers deployment, configs and how the distills compare to base Llama 3 8B on a Gigagpu UK GPU node.

Contents

• The distill series
• VRAM and precision
• Throughput numbers
• Reasoning vs base Llama 3 8B
• Deployment
• Reasoning-mode tips

The distill series

DeepSeek distilled R1’s reasoning traces into six smaller dense checkpoints. The two that fit comfortably in 16 GB with production-grade throughput are the 7B (Qwen2.5 base) and 8B (Llama 3.1 base) variants.

VRAM and precision

Distill models think a lot – typical traces run 4k-16k reasoning tokens before the final answer – so KV cache sizing matters more than for ordinary chat.

Throughput numbers

Measured with vLLM 0.6, reasoning-style output (8k tokens) on the 5060 Ti 16GB.

Reasoning vs base Llama 3 8B

DeepSeek-R1-Distill-Llama-8B shares an architecture with Meta’s base Llama 3.1 8B, so it’s a clean A/B test. Raw throughput is essentially identical (108 vs 112 t/s) because the architecture is the same – the difference is output quality on reasoning-hard tasks.

The gap on general knowledge (MMLU) is small, but on any task that requires chain-of-thought the distill is transformative – and at essentially the same token cost.

Deployment

docker run -d --gpus all -p 8000:8000 vllm/vllm-openai:v0.6.3 \
  --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
  --quantization fp8 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.85 \
  --enable-prefix-caching

Reasoning-mode tips

• Set max_tokens generously – 8k-16k – or reasoning traces get truncated before the final answer.
• Use temperature 0.6 with top_p 0.95 as the DeepSeek paper recommends; greedy decoding degrades reasoning quality.
• Strip the <think>…</think> block from user-facing responses but keep it server-side for debugging.
• Budget for roughly 10x more output tokens than a non-reasoning model; factor that into cost planning.
• Distill-Qwen-7B is usually the best VRAM/quality tradeoff on a single 16 GB card.

See also: Llama 3 8B benchmark, Qwen 14B benchmark, FP8 Llama deployment, vLLM setup, prefix caching.