RTX 5060 Ti 16GB Native FP8 Support

The RTX 5060 Ti 16GB inherits Blackwell’s 5th-gen tensor cores with native FP8 support. For AI serving on our dedicated GPU hosting this is one of the most practical features at the mid-tier price point. Here is what FP8 actually delivers.

Contents

• What FP8 is
• Memory savings
• Speed gains
• Quality impact
• Checkpoints
• Deployment

What FP8 Is

FP8 is an 8-bit floating-point format. Two variants in widespread use:

• E4M3: 4 exponent bits, 3 mantissa. Better precision, smaller range. Used for weights and activations in forward pass.
• E5M2: 5 exponent bits, 2 mantissa. Wider range, less precision. Used for gradients in training.

Blackwell tensor cores accelerate both natively. FP8 lives in the spec sheet at ~400 TFLOPS on the 5060 Ti – twice the FP16 rate.

Memory Savings

FP8 weights are half the size of FP16. Concrete examples:

Speed

With FP8 tensor cores running native, throughput roughly matches or beats FP16 while using half the memory. On Mistral 7B:

• FP16 decode on 5060 Ti: ~65 t/s (tight VRAM limits batching)
• FP8 decode on 5060 Ti: ~110 t/s

The FP8 win is ~70% faster on Mistral 7B not because the raw matmul is faster, but because reduced memory traffic + native tensor core path + more KV cache for concurrent batching stack up.

Quality

FP8 quality degradation on perplexity benchmarks is typically 0.3-0.8% versus FP16. On downstream eval (MMLU, HumanEval), often indistinguishable. For most production workloads users cannot tell the difference.

Exceptions where quality loss is visible:

• Very long-context reasoning with accumulated error
• Precision-critical math or finance workloads
• Models not specifically trained/fine-tuned with FP8 in mind

Checkpoints

Pre-quantised FP8 checkpoints widely available:

• neuralmagic/Llama-3.1-8B-Instruct-FP8
• neuralmagic/Llama-3.3-70B-Instruct-FP8 (needs larger card)
• neuralmagic/Mistral-7B-Instruct-FP8
• neuralmagic/Qwen2.5-14B-Instruct-FP8
• Diffusion: FLUX Schnell in FP8

Deployment

vLLM:

python -m vllm.entrypoints.openai.api_server \
  --model neuralmagic/Llama-3.1-8B-Instruct-FP8 \
  --quantization fp8 \
  --max-model-len 16384 \
  --gpu-memory-utilization 0.92

TGI:

text-generation-launcher \
  --model-id neuralmagic/Mistral-7B-Instruct-FP8 \
  --quantize fp8

For more format options see quantisation formats compared.

See also: FP8 Llama deployment, 5th-gen tensor cores.