Eighteen tokens per second does not sound like much on paper, but it is the threshold where LLM responses start to feel genuinely conversational. That is exactly what the RTX 4060 delivers when running Meta’s LLaMA 3 8B — fast enough for real-time chat, affordable enough to leave running around the clock. We benchmarked this combination on GigaGPU dedicated servers to see whether the 4060 hits the sweet spot between price and performance.
Measured Throughput
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 18 tok/s |
| Tokens/sec (batched, bs=8) | 23.4 tok/s |
| Per-token latency | 55.6 ms |
| Precision | INT4 |
| Quantisation | 4-bit GGUF Q4_K_M |
| Max context length | 4K |
| Performance rating | Good |
Benchmark conditions: single-stream generation, 512-token prompt, 256-token completion, llama.cpp or vLLM backend. GGUF Q4_K_M via llama.cpp or vLLM FP16.
The Ada Lovelace architecture in the 4060 gives it a significant efficiency advantage over the older 3050. Even though we are still running 4-bit quantised weights, the 18 tok/s single-stream rate is more than double what the RTX 3050 manages. Batched throughput climbs to 23.4 tok/s, which means a small team of 2-3 users can share the GPU without noticeable slowdown.
Memory Allocation Breakdown
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 5.5 GB |
| KV cache + runtime | ~0.8 GB |
| Total RTX 4060 VRAM | 8 GB |
| Free headroom | ~2.5 GB |
The 8GB frame buffer leaves a comfortable 2.5 GB free after loading the quantised model. That extra headroom compared to the 3050 means more stable operation under load and room for slightly larger KV caches, though you are still capped at 4K context with this quantisation level.
Running Costs
| Cost Metric | Value |
|---|---|
| Server cost | £0.35/hr (£69/mo) |
| Cost per 1M tokens | £5.401 |
| Tokens per £1 | 185151 |
| Break-even vs API | ~1 req/day |
At £5.40 per million tokens single-stream, the RTX 4060 already beats most commercial API pricing. With batching, that drops to about £3.38 per million tokens — roughly half the cost of the RTX 3050 on a per-token basis. The monthly flat rate of £69 means you break even against pay-per-token APIs with surprisingly light usage. Check our tokens-per-second benchmark for detailed comparisons.
The Verdict
The RTX 4060 is the entry point where LLaMA 3 8B becomes genuinely useful for small-scale production. It handles development workloads with ease and can serve light API traffic for internal tools. If you need longer context windows or heavier batching, the RTX 4060 Ti with its 16GB opens up FP16 inference — but for raw cost efficiency at moderate volumes, the 4060 is hard to beat.
Quick deploy:
docker run --gpus all -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/llama-3-8b.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99For more details, see our LLaMA hosting guide and best GPU for LLaMA. Compare with the DeepSeek 7B on RTX 4060, or browse all benchmarks.
Run LLaMA 3 8B on RTX 4060
The budget-friendly workhorse for LLM inference. UK datacenter, root access, £69/mo.
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