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DeepSeek Concurrency Overview
DeepSeek R1 Distill 7B has become a popular choice for reasoning-heavy tasks on dedicated GPU servers thanks to its strong performance on coding, maths, and logical inference. For production deployment, you need to understand how throughput scales as you add concurrent users. We tested DeepSeek R1 Distill 7B (INT4, GPTQ) from 1 to 64 concurrent requests across four GPUs.
Tests ran on GigaGPU bare-metal servers using vLLM continuous batching. Each request used a 128-token prompt with 256-token output. For single-user speed data, 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 | 0.08 | 0.22 | 0.32 | 0.46 |
| 4 | 0.26 | 0.76 | 1.15 | 1.70 |
| 8 | 0.42 | 1.35 | 2.10 | 3.10 |
| 16 | 0.56 | 2.30 | 3.50 | 5.20 |
| 32 | 0.62 | 3.40 | 4.85 | 7.40 |
| 64 | OOM | 4.10 | 5.60 | 8.90 |
DeepSeek R1 Distill peaks at 8.9 req/s on the RTX 5090 at concurrency 64. The RTX 3090 handles 4.1 req/s at the same level — enough for over 10 million requests per month. The RTX 4060 tops out before concurrency 64 due to VRAM constraints.
Per-Request Latency Curve
| Concurrency | RTX 4060 (e2e p50) | RTX 3090 (e2e p50) | RTX 5080 (e2e p50) | RTX 5090 (e2e p50) |
|---|---|---|---|---|
| 1 | 12.5 s | 4.5 s | 3.1 s | 2.2 s |
| 4 | 15.4 s | 5.3 s | 3.5 s | 2.4 s |
| 8 | 19.0 s | 5.9 s | 3.8 s | 2.6 s |
| 16 | 28.5 s | 7.0 s | 4.6 s | 3.1 s |
| 32 | 51.5 s | 9.4 s | 6.6 s | 4.3 s |
| 64 | OOM | 15.6 s | 11.4 s | 7.2 s |
DeepSeek’s per-request latency is 5-10 percent higher than LLaMA 3 8B at the same concurrency levels, which is expected given its slightly different architecture. On the RTX 3090, latency remains under 10 seconds up to concurrency 32 — acceptable for background processing and API workloads. For interactive user-facing latency targets, see the RTX 3090 concurrent users guide.
DeepSeek vs LLaMA 3 8B Scaling
Compared to LLaMA 3 8B concurrent throughput, DeepSeek R1 Distill 7B is roughly 8-10 percent slower at every concurrency level. The difference is consistent and comes from DeepSeek’s deeper transformer layers which require more compute per token. Both models have similar VRAM footprints at INT4 (4-5 GB), so KV cache capacity and thus maximum concurrency are nearly identical.
For tasks that benefit from DeepSeek’s stronger reasoning capabilities — coding assistants, mathematical tutoring, logical analysis — the 8-10 percent throughput cost is easily justified by the quality improvement. For general-purpose chatbots where reasoning depth is less critical, LLaMA 3 8B offers slightly better throughput per pound. See the best GPU for LLM inference guide for model selection advice.
Production Deployment Tips
DeepSeek R1 Distill models work well with vLLM’s continuous batching and benefit from the same tuning strategies as other 7B models. Set --max-model-len to match your actual context needs (often 2048-4096 is sufficient), enable prefix caching for shared system prompts, and use AWQ or GPTQ INT4 quantisation for the best throughput-to-quality ratio.
For step-by-step deployment instructions, follow the vLLM production setup guide. If you need a simpler deployment path, Ollama supports DeepSeek models out of the box with less configuration overhead. For cost modelling, use the LLM cost calculator.
Conclusion
DeepSeek R1 Distill 7B scales well from 1 to 64 concurrent requests, reaching 8.9 req/s on the RTX 5090 and 4.1 req/s on the RTX 3090. Its throughput is 8-10 percent lower than LLaMA 3 8B but delivers stronger reasoning quality. For additional model comparisons, see the Mistral 7B concurrent throughput benchmark and the RTX 3090 vs RTX 5080 throughput per dollar analysis in the Benchmarks category.
