Table of Contents
Mistral 7B Scaling Overview
Mistral 7B is the throughput leader among 7B-class models thanks to its grouped-query attention (GQA) architecture, which reduces KV cache memory per sequence and allows more concurrent requests to fit in VRAM. We tested how this architectural advantage translates to real-world concurrent throughput on dedicated GPU servers using vLLM continuous batching.
All benchmarks ran on GigaGPU bare-metal servers with Mistral 7B (INT4, GPTQ). Each request used a 128-token prompt with 256-token output. For single-user speed comparisons, see the tokens per second benchmark.
Throughput by Concurrency Level
| Concurrency | RTX 4060 (req/s) | RTX 3090 (req/s) | RTX 5080 (req/s) | RTX 5090 (req/s) |
|---|---|---|---|---|
| 1 | 0.10 | 0.26 | 0.38 | 0.55 |
| 4 | 0.34 | 0.90 | 1.38 | 2.00 |
| 8 | 0.55 | 1.60 | 2.50 | 3.65 |
| 16 | 0.72 | 2.75 | 4.15 | 6.10 |
| 32 | 0.80 | 4.00 | 5.70 | 8.60 |
| 64 | OOM | 4.80 | 6.60 | 10.30 |
Mistral 7B reaches 10.3 req/s on the RTX 5090 at concurrency 64 — the highest throughput of any 7B-class model we have tested. On the RTX 3090, it peaks at 4.8 req/s, approximately 7 percent faster than LLaMA 3 8B. For the RTX 3090’s maximum throughput details, see the RTX 3090 throughput benchmark.
Per-Request Latency Curve
| Concurrency | RTX 4060 (e2e p50) | RTX 3090 (e2e p50) | RTX 5080 (e2e p50) | RTX 5090 (e2e p50) |
|---|---|---|---|---|
| 1 | 10.0 s | 3.8 s | 2.6 s | 1.8 s |
| 4 | 11.8 s | 4.4 s | 2.9 s | 2.0 s |
| 8 | 14.5 s | 5.0 s | 3.2 s | 2.2 s |
| 16 | 22.0 s | 5.8 s | 3.9 s | 2.6 s |
| 32 | 40.0 s | 8.0 s | 5.6 s | 3.7 s |
| 64 | OOM | 13.3 s | 9.7 s | 6.2 s |
Mistral 7B’s GQA architecture gives it a latency advantage at high concurrency. At concurrency 64 on the RTX 3090, Mistral’s 13.3 s per request is about 6 percent faster than LLaMA 3 8B’s 14.2 s. The advantage grows at higher concurrency because fewer KV heads mean less memory bandwidth pressure per sequence.
Mistral vs LLaMA vs DeepSeek Scaling
Across all concurrency levels and GPUs, Mistral 7B consistently delivers the highest throughput of the three 7B-class models we benchmark. The rankings are consistent: Mistral 7B leads by 5-8 percent over LLaMA 3 8B, which in turn leads DeepSeek R1 Distill 7B by 8-10 percent. The throughput differences are driven by architecture, not model quality — all three are excellent at their respective strengths.
If throughput per pound is your primary concern and general-purpose chat quality is sufficient, Mistral 7B is the optimal choice. For stronger reasoning tasks, DeepSeek justifies its throughput penalty. For the broadest general capability, LLaMA 3 8B sits in the middle. See the best GPU for LLM inference guide for model selection context.
Optimal Concurrency by GPU
For chatbot applications targeting 5-second end-to-end latency, the optimal operating points for Mistral 7B are: RTX 4060 at concurrency 1-3, RTX 3090 at concurrency 10-16, RTX 5080 at concurrency 20-24, and RTX 5090 at concurrency 32-40. These ranges deliver near-maximum throughput while keeping latency within interactive bounds.
Use the LLM cost calculator to model costs at these concurrency levels. For deployment, the vLLM production setup guide covers configuration for optimal concurrent serving. Broader capacity planning is covered in our GPU capacity planning for AI SaaS guide.
Conclusion
Mistral 7B is the throughput king among 7B models, reaching 10.3 req/s on the RTX 5090 and 4.8 req/s on the RTX 3090 at concurrency 64. Its GQA architecture provides a consistent 5-8 percent advantage over LLaMA 3 8B at every concurrency level. Compare throughput-per-dollar across GPUs in the RTX 3090 vs RTX 5090 comparison, or browse all model benchmarks in the Benchmarks category.
