Microsoft’s Phi-3 Mini packs 3.8 billion parameters into a footprint so small that it leaves 2.9 GB of VRAM free on a 6 GB card. That is unusual — most models that fit on the RTX 3050 are either too small to be useful or too compressed to be accurate. Phi-3 Mini threads the needle, delivering reasoning and instruction-following quality that punches well above its parameter count, at 8 tok/s on a GigaGPU dedicated server. For edge-style deployments, embedded assistants, and ultra-low-cost inference, these numbers tell an interesting story.
Phi-3 Mini Performance on RTX 3050
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 8 tok/s |
| Tokens/sec (batched, bs=8) | 10.4 tok/s |
| Per-token latency | 125.0 ms |
| Precision | INT4 |
| Quantisation | 4-bit GGUF Q4_K_M |
| Max context length | 4K |
| Performance rating | Acceptable |
Benchmark conditions: single-stream generation, 512-token prompt, 256-token completion, llama.cpp or vLLM backend. GGUF Q4_K_M via llama.cpp or ONNX Runtime.
VRAM: The Smallest Model, the Most Headroom
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 3.1 GB |
| KV cache + runtime | ~0.5 GB |
| Total RTX 3050 VRAM | 6 GB |
| Free headroom | ~2.9 GB |
Phi-3 Mini’s 3.1 GB weight footprint is roughly half that of Qwen 2.5 7B on the same card. The 2.9 GB of free headroom is genuinely useful: you can run longer system prompts, maintain larger KV caches for multi-turn conversations, or even experiment with running a small embedding model alongside Phi-3 Mini for a lightweight RAG setup. No other model in its quality class leaves this much room on a 6 GB card.
Cost Efficiency: The Cheapest Useful LLM
| Cost Metric | Value |
|---|---|
| Server cost | £0.25/hr (£49/mo) |
| Cost per 1M tokens | £8.681 |
| Tokens per £1 | 115194 |
| Break-even vs API | ~1 req/day |
The £8.681 per 1M tokens is the highest single-stream cost in this benchmark set, but the £49/mo flat rate is the absolute floor for self-hosted LLM inference. With batched inference (bs=8), effective cost drops to ~£5.426 per 1M tokens. The value proposition is not per-token economics but total cost of ownership: for £49/mo you get a private, always-on LLM server with no per-request charges, no data leaving your infrastructure, and no rate limits. For personal projects, internal tools, or privacy-sensitive prototypes, that flat rate is hard to beat. See our full tokens-per-second benchmark for cross-GPU comparisons.
Where Phi-3 Mini on RTX 3050 Shines
This configuration is purpose-built for scenarios where cost and privacy outweigh throughput: personal coding assistants, private journal summarisers, small-business FAQ bots, or educational tools where the 8 tok/s output speed matches the pace at which users actually read. If your use case needs faster generation or more concurrent users, step up to the RTX 4060 or above.
Quick deploy:
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 99For more setup details, see our Phi-3 Mini hosting guide and best GPU for Phi-3. You can also check all benchmark results, or the LLaMA 3 8B on RTX 3050 benchmark.
Deploy Phi-3 Mini on RTX 3050
Order this exact configuration. UK datacenter, full root access.
Order RTX 3050 Server
