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RTX 4060 Concurrency Overview
The RTX 4060 is the most affordable option in the GigaGPU dedicated GPU hosting lineup. With 8 GB of GDDR6 VRAM, it is constrained for large models but surprisingly capable for small quantised LLMs. We tested how many concurrent users it can serve while maintaining acceptable latency thresholds.
All tests used vLLM continuous batching on a GigaGPU bare-metal server. Each user sent a 128-token prompt and received a 256-token completion. Concurrency was ramped until p99 time to first token (TTFT) breached each latency target. For baseline throughput data, see the tokens per second benchmark.
Concurrent Users by Latency Target
With only 8 GB of VRAM, the RTX 4060 is limited to INT4 quantised models at 7-8B parameters. Larger models or higher-precision quantisations do not leave enough VRAM for meaningful KV cache.
| Model (Quantisation) | ≤ 200 ms TTFT | ≤ 500 ms TTFT | ≤ 1 s TTFT | ≤ 2 s TTFT |
|---|---|---|---|---|
| LLaMA 3 8B (INT4) | 2 | 4 | 7 | 11 |
| Mistral 7B (INT4) | 2 | 5 | 8 | 12 |
| Phi-3 Mini 3.8B (INT4) | 4 | 9 | 15 | 24 |
| Qwen 2.5 3B (INT4) | 5 | 10 | 16 | 26 |
| Gemma 2 2B (INT4) | 6 | 12 | 20 | 30 |
At the 500 ms TTFT target, the RTX 4060 handles 4-5 concurrent users with 7B INT4 models, scaling to 9-12 users with smaller 2-4B models. That makes it suitable for development, internal tools, and very low-traffic production endpoints. For more headroom, the RTX 3090 concurrency results show a 3x improvement.
The 8 GB VRAM Constraint
The primary bottleneck is VRAM, not compute. A 7B INT4 model consumes roughly 4-4.5 GB of weights, leaving only 3-3.5 GB for KV cache, vLLM overhead, and CUDA context. Each concurrent sequence in vLLM’s KV cache uses around 0.3-0.5 GB (depending on context length), which limits practical concurrency to single digits.
FP16 models are essentially off the table for concurrent serving — a 7B FP16 model at 14-16 GB does not fit at all. INT8 models at around 7-8 GB leave almost no KV cache room. The 4060 is strictly an INT4-only card for production LLM serving. For a deeper analysis of quantisation trade-offs, see our FP16 vs INT8 vs INT4 speed comparison.
Best Models for the RTX 4060
To get the most out of 8 GB, focus on sub-4B parameter models. Phi-3 Mini (3.8B) and Qwen 2.5 3B deliver surprisingly strong quality at INT4 with weights under 2.5 GB, leaving 5+ GB for KV cache. This pushes concurrent users to 9-10 at the 500 ms latency target — enough for a lightweight customer support bot or internal knowledge assistant.
For embedding workloads and retrieval-augmented generation, the RTX 4060 handles embedding models efficiently because they use minimal VRAM and process requests in large batches. You might also consider running Ollama for simpler single-model deployments where vLLM’s complexity is not warranted.
When to Upgrade
If your concurrency needs exceed 5-6 users at sub-500 ms latency with 7B models, the RTX 4060 is not the right card. The RTX 4060 vs RTX 3090 throughput per dollar comparison shows that the 3090 offers roughly 3x the concurrent capacity at around 1.5x the monthly cost — a clear value advantage for production workloads.
The RTX 4060 excels as a development and testing server, a low-traffic production endpoint, or a dedicated card for lightweight AI tasks like OCR, embedding generation, or small model inference. For capacity planning at scale, see our GPU capacity planning for AI SaaS guide.
Conclusion
The RTX 4060 is the entry point for self-hosted LLM inference. It handles 4-5 concurrent users with 7B INT4 models and up to 12 users with smaller 2-3B models at 500 ms TTFT. For prototyping, internal tools, and lightweight production use, it delivers genuine value at the lowest price tier in the GPU comparisons lineup. When you need more capacity, scaling to a RTX 3090 is the natural next step.
