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RTX 3090 Concurrency Overview
When you are sizing a dedicated GPU server for an LLM-powered product, raw tokens per second only tells half the story. What matters in production is how many users your GPU can serve simultaneously while keeping response latency acceptable. The RTX 3090 with its 24 GB of VRAM remains one of the most popular choices for self-hosted inference, so we tested exactly how many concurrent users it handles across several models and latency budgets.
All tests used vLLM with continuous batching enabled on a GigaGPU bare-metal server. We sent concurrent requests at steadily increasing levels and measured time to first token (TTFT) and end-to-end latency at p50 and p99 percentiles. For single-user speed baselines, see the tokens per second benchmark.
Concurrent Users by Latency Target
The table below shows the maximum number of concurrent users the RTX 3090 supports before crossing each latency threshold. These numbers assume 256-token outputs with continuous batching.
| Model (Quantisation) | ≤ 200 ms TTFT | ≤ 500 ms TTFT | ≤ 1 s TTFT | ≤ 2 s TTFT |
|---|---|---|---|---|
| LLaMA 3 8B (INT4) | 6 | 14 | 24 | 38 |
| LLaMA 3 8B (FP16) | 3 | 8 | 14 | 22 |
| Mistral 7B (INT4) | 7 | 15 | 26 | 40 |
| Mistral 7B (FP16) | 4 | 9 | 16 | 24 |
| DeepSeek R1 Distill 7B (INT4) | 5 | 12 | 20 | 32 |
| Qwen 2.5 7B (INT4) | 6 | 14 | 23 | 36 |
At a comfortable 500 ms TTFT target, the RTX 3090 handles roughly 8-15 concurrent users depending on model and quantisation. That is enough for an internal tool or early-stage SaaS product. For broader context on how the 3090 compares, see our best GPU for LLM inference guide.
Model-by-Model Breakdown
Smaller models leave more VRAM headroom for the KV cache, which directly increases the maximum number of concurrent sequences vLLM can batch together. LLaMA 3 8B in INT4 uses roughly 4.5 GB of model weights, leaving over 19 GB for KV cache and overhead, which is why it comfortably handles 14 users under 500 ms.
Running the same model at FP16 doubles the weight footprint to around 16 GB, cutting KV cache space almost in half and limiting practical concurrency to about 8 users at the same latency target. If you are comparing quantisation trade-offs, our FP16 vs INT8 vs INT4 speed comparison covers the quality-versus-throughput balance in detail.
For models that push the 24 GB ceiling (such as 13B FP16 or 34B quantised), concurrency drops sharply because KV cache space becomes the bottleneck. If you need to run larger models, the RTX 3090 vs RTX 5090 comparison shows where the extra VRAM pays off.
Scaling Beyond One GPU
When a single RTX 3090 cannot meet your concurrency requirements, you have two scaling paths. Horizontal scaling deploys the same model on multiple independent GPUs behind a load balancer — this scales concurrency linearly and is the simplest option. Vertical scaling with multi-GPU tensor parallelism splits a single large model across GPUs, which is necessary for models that exceed one card’s VRAM but does not linearly increase concurrent user capacity.
For most 7-8B model deployments, horizontal scaling is the better choice. Two RTX 3090 servers with a load balancer roughly double the numbers in the table above. See our 1 GPU vs 2 GPU scaling guide for a deeper breakdown.
Tuning for Maximum Concurrency
Several vLLM settings directly affect how many users fit on one card. Increasing --max-num-seqs allows more concurrent sequences but increases memory pressure. Reducing --max-model-len from the default (often 4096 or higher) to match your actual use case frees KV cache space. Enabling --enable-prefix-caching helps when many requests share a common system prompt, which is typical for chatbot deployments.
Batch size also plays a significant role in overall throughput. Our batch size impact on tokens/sec analysis shows the relationship between batch size and per-request latency across different GPUs. For step-by-step deployment instructions, follow our vLLM production setup guide.
Conclusion
The RTX 3090 is a capable card for serving LLMs to moderate numbers of concurrent users. With INT4 quantised 7-8B models and vLLM continuous batching, expect 12-15 users at sub-500 ms time to first token, or up to 38-40 users if your application can tolerate up to 2 seconds of initial latency. For higher concurrency needs, consider the RTX 3090 vs RTX 5090 throughput per dollar comparison to decide whether upgrading or adding a second card offers better value.
