At just 3.8 billion parameters, Microsoft’s Phi-3 Mini punches well above its weight in reasoning tasks. But does the budget-friendly RTX 4060 give it enough room to stretch? We benchmarked the pairing on a GigaGPU dedicated server to find out.
Benchmark Results
| 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 |
Testing used single-stream generation with a 512-token prompt and 256-token completion via llama.cpp, running the Q4_K_M GGUF quantisation.
Why 4-bit Quantisation Matters Here
The RTX 4060 ships with 8 GB of VRAM. Phi-3 Mini’s full FP16 weights occupy roughly 8 GB on their own, leaving nothing for KV cache or runtime overhead. Dropping to Q4_K_M cuts the weight footprint to about 3.1 GB, freeing up nearly 4.9 GB for context handling and concurrent sessions. That trade-off barely dents output quality for most inference tasks.
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 3.1 GB |
| KV cache + runtime | ~0.5 GB |
| Total RTX 4060 VRAM | 8 GB |
| Free headroom | ~4.9 GB |
What Does It Cost?
| Cost Metric | Value |
|---|---|
| Server cost | £0.35/hr (£69/mo) |
| Cost per 1M tokens | £5.401 |
| Tokens per £1 | 185,151 |
| Break-even vs API | ~1 req/day |
Single-stream throughput works out to £5.40 per million tokens. Batch eight requests together and that drops to roughly £3.38/M. Compare that against hosted API endpoints charging £0.50–2.00+ per million tokens. At £69/mo flat, the RTX 4060 pays for itself almost immediately for any regular workload. Check our tokens-per-second benchmark tool to compare across GPUs.
Who Should Use This Setup?
Eighteen tokens per second is fast enough for interactive chat during development, internal tools, or low-traffic customer-facing bots. It is not the right pick for high-concurrency production APIs — for that, step up to the RTX 3090. But for prototyping, fine-tuning experiments, or a staging environment, the 4060 keeps costs low without starving the model.
Get started in one command:
docker run --gpus all -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/phi-3-mini.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99More configuration details live in our Phi-3 hosting guide. You might also want to read the best GPU for LLM inference roundup, browse all benchmarks, or see how the cheapest GPU options stack up.
Run Phi-3 Mini on an RTX 4060 Today
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