How We Optimized Llama 3 for 200 Tokens/Sec
Discover the architectural changes, custom CUDA kernels, and tensor parallel setups we implemented to make Llama 3 70B run blazingly fast.
How We Optimized Llama 3 for 200 Tokens/Sec
At DeepTox, we pride ourselves on offering not just the cheapest inference, but also the fastest. When Meta released Llama 3, we immediately set out to achieve the highest possible output speed. Here is how we achieved **200 tokens per second** on Llama 3 70B.
1. Custom Tensor Parallelism
Standard huggingface transformers run tensor parallelism using generic libraries that incur high communication overhead. By writing custom communication collectives using NCCL and tailoring them specifically to our H100 GPU clusters, we reduced latency by **35%**.
2. FlashAttention-3 Integration
We integrated custom pre-release FlashAttention kernels. This optimization dramatically decreases the memory bandwidth bottlenecks in the KV cache, allowing large batch sizes to sustain high token generation rates.
3. Dynamic Quantization
We serve Llama 3 using dynamic FP8 quantization. This reduces the model memory footprint while keeping perplexity loss virtually indistinguishable from FP16.
# Simple example client code
import openai
client = openai.OpenAI(
base_url="https://api.deeptox.com/v1/openai",
api_key="YOUR_DEEPTOX_API_KEY"
)
chat_completion = client.chat.completions.create(
model="meta-llama/Llama-3-70b-instruct",
messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}]
)
print(chat_completion.choices[0].message.content)
We are excited to bring these optimizations to our users at a fraction of the cost of other providers.