When your production API gets mentioned on Hacker News and traffic spikes 10x, you need to know exactly how many requests per second your model can handle before latency explodes. We load-tested LLaMA 3 8B and Qwen 2.5 7B to find the breaking point on a single dedicated GPU.
Sustained Load Test
RTX 3090, vLLM, INT4, continuous batching, ramped from 1 to 50 concurrent connections. Live benchmark data.
| Model (INT4) | Requests/sec | p50 Latency (ms) | p99 Latency (ms) | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 35.0 | 79 | 354 | 6.5 GB |
| Qwen 2.5 7B | 13.8 | 100 | 260 | 5.8 GB |
This one is not close. LLaMA handles 2.5 times more requests per second — 35 versus 13.8. The median latency is also faster at 79 ms versus 100 ms. Where Qwen does better is tail latency: its p99 of 260 ms is significantly tighter than LLaMA’s 354 ms. This means Qwen never gets as slow as LLaMA does on its worst requests, but it serves far fewer total requests.
Why Such a Large Throughput Gap?
| Specification | LLaMA 3 8B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 128K |
| VRAM (FP16) | 16 GB | 15 GB |
| VRAM (INT4) | 6.5 GB | 5.8 GB |
| Licence | Meta Community | Apache 2.0 |
Qwen’s 128K context window requires a larger KV cache allocation per request, even when the actual prompt is short. vLLM reserves memory based on the model’s maximum context length, which means Qwen’s larger window eats into the memory available for batching. Fewer concurrent batches equals lower throughput. LLaMA’s modest 8K window is actually an advantage here — it leaves more VRAM for the batch scheduler. Details in the LLaMA VRAM guide and Qwen VRAM guide.
Cost Comparison
| Cost Factor | LLaMA 3 8B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £143 | £85 |
| Throughput Advantage | 8% faster | 8% cheaper/tok |
LLaMA’s 2.5x throughput advantage means dramatically lower cost per request at scale. One LLaMA server replaces roughly two and a half Qwen servers for the same traffic volume. Use the cost calculator for precise projections.
When to Pick Which
LLaMA 3 8B for high-throughput APIs. If you are building an endpoint that needs to absorb traffic spikes and your prompts are short (under 4K tokens), LLaMA’s throughput advantage is decisive. The savings in GPU count at scale are substantial. More hardware details at best GPU for inference.
Qwen 2.5 7B for long-context APIs. If your API processes large documents or long conversation histories where the 128K context window is actually utilised, Qwen’s lower tail latency and superior accuracy on long-context tasks make it the better choice — you just need to provision more GPUs for the traffic. See the comparisons hub for related matchups.
Both deploy cleanly behind vLLM on dedicated hardware.
See also: LLaMA 3 vs Qwen for Chatbots | LLaMA 3 vs DeepSeek for API Serving
Scale Your API
Run LLaMA 3 8B or Qwen 2.5 7B on dedicated GPU servers. No rate limits, no noisy neighbours.
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