Choosing the wrong model for a production API is expensive — not because the GPU costs change, but because you end up buying a second server months later when traffic grows. We load-tested Mistral 7B and Qwen 2.5 7B under sustained concurrent traffic to reveal which model gives you the most runway on a single dedicated GPU.
Hardware and Model Specs
| Specification | Mistral 7B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Architecture | Dense Transformer + SWA | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14.5 GB | 15 GB |
| VRAM (INT4) | 5.5 GB | 5.8 GB |
| Licence | Apache 2.0 | Apache 2.0 |
Qwen’s 128K context window creates larger KV-cache entries per request, which limits how many sequences vLLM can batch simultaneously. Mistral’s SWA keeps per-sequence memory predictable and lower. Explore more: Mistral VRAM | Qwen VRAM.
Throughput Under Concurrent Load
RTX 3090, vLLM, INT4 quantisation, continuous batching, 80 concurrent clients, 128-token average output. Live metrics: tokens-per-second benchmark.
| Model (INT4) | Requests/sec | p50 Latency (ms) | p99 Latency (ms) | VRAM Used |
|---|---|---|---|---|
| Mistral 7B | 15.3 | 84 | 366 | 5.5 GB |
| Qwen 2.5 7B | 33.5 | 50 | 363 | 5.8 GB |
Qwen delivers a surprise here: more than double Mistral’s throughput (33.5 vs 15.3 req/s) with a much lower median latency (50 ms vs 84 ms). Despite the larger context window, Qwen’s dense transformer handles vLLM’s continuous batching more efficiently at this concurrency level. The p99 latencies are nearly identical (~365 ms), so tail behaviour is comparable.
Related: Mistral vs Qwen for Chatbots | LLaMA 3 vs Mistral for API Serving
Cost Implications
| Cost Factor | Mistral 7B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £111 | £172 |
| Throughput Advantage | 7% faster | 10% cheaper/tok |
One Qwen server at £172/month handles the traffic that would require two Mistral servers at £222/month combined. For high-traffic endpoints, Qwen’s throughput advantage translates into real savings. Model it: cost-per-million-tokens calculator.
The Decision
Qwen 2.5 7B is the better API serving model. Double the throughput and lower median latency make it the obvious choice for any production API expecting more than 15 requests per second. It handles growth gracefully — you will not need a second GPU until traffic roughly quadruples.
Mistral 7B is the right pick only for low-traffic APIs (under 15 req/s) where you value Mistral’s response style or need the lower VRAM footprint to share the GPU with another model. Its SWA architecture also excels at long-output generation where per-token speed matters more than batch throughput.
Serve either model on dedicated GPU hosting with vLLM and continuous batching. More guidance: best GPU for LLM inference | all comparisons.
Serve Your LLM API
Deploy Mistral 7B or Qwen 2.5 7B on bare-metal GPUs with full root access and no per-request pricing.
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