Google’s Gemma 2 9B has earned a reputation for punching above its weight on reasoning and instruction following. The question for teams on a budget: does the RTX 4060 give it enough breathing room to be useful? We ran full benchmarks on a GigaGPU dedicated server to answer that.
Benchmark Numbers
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 18.5 tok/s |
| Tokens/sec (batched, bs=8) | 24.1 tok/s |
| Per-token latency | 54.1 ms |
| Precision | INT4 |
| Quantisation | 4-bit GGUF Q4_K_M |
| Max context length | 4K |
| Performance rating | Good |
512-token prompt, 256-token completion, single-stream, llama.cpp with Q4_K_M. The 4060 more than doubles the RTX 3050’s throughput for this model, crossing into comfortably interactive territory.
Memory Situation
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 6.4 GB |
| KV cache + runtime | ~1.0 GB |
| Total RTX 4060 VRAM | 8 GB |
| Free headroom | ~1.6 GB |
The fit is tight but workable. Unlike the 3050, all model layers sit entirely on the GPU, so you avoid the performance penalty of partial CPU offloading. That said, the 1.6 GB of remaining headroom limits you to shorter contexts (4K) and single-stream inference. Concurrent requests at this memory level are risky.
What It Costs
| Cost Metric | Value |
|---|---|
| Server cost | £0.35/hr (£69/mo) |
| Cost per 1M tokens | £5.255 |
| Tokens per £1 | 190,295 |
| Break-even vs API | ~1 req/day |
£5.26 per million tokens single-stream, dropping to about £3.28/M with batched inference. At £69/mo, the RTX 4060 is the cheapest card that runs Gemma 2 9B without resorting to partial offloading. For side-by-side GPU pricing, check the cost-per-million-tokens tool.
Best Use Cases
At 18.5 tok/s, responses feel responsive enough for development workflows, prompt engineering, and light internal tools. Production-facing chat applications will want more headroom — the RTX 4060 Ti with 16 GB opens up FP16 and longer contexts. But for experimentation and validation at minimal cost, the 4060 gets the job done.
Launch command:
docker run --gpus all -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/gemma-2-9b.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99Setup details in the Gemma hosting guide. Also: best GPU for LLM inference, all benchmarks, tok/s comparison.
Run Gemma 2 9B on the RTX 4060
Budget-friendly 9B inference without CPU offloading. UK datacentre, flat rate.
Get Your RTX 4060
