Sixteen gigabytes of VRAM changes what is possible with Gemma 2 9B. While the smaller 40-series cards force aggressive quantisation and short contexts, the RTX 4060 Ti finally gives this 9-billion-parameter model proper breathing room. Here is how it performs on GigaGPU dedicated hardware.
Measured Performance
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 23.6 tok/s |
| Tokens/sec (batched, bs=8) | 30.7 tok/s |
| Per-token latency | 42.4 ms |
| Precision | INT4 |
| Quantisation | 4-bit GGUF Q4_K_M |
| Max context length | 8K |
| Performance rating | Good |
Tested single-stream, 512-token prompt, 256-token completion via llama.cpp Q4_K_M. Notably, the 4060 Ti enables 8K context — double what the 4060 can manage with this model.
VRAM Allocation
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 6.4 GB |
| KV cache + runtime | ~1.0 GB |
| Total RTX 4060 Ti VRAM | 16 GB |
| Free headroom | ~9.6 GB |
Nearly 10 GB free after the model loads. That surplus is enough to extend context further, handle a handful of concurrent requests, or co-host a lightweight secondary model. The 4060 Ti is the first card in the lineup that makes Gemma 2 9B feel unconstrained at 4-bit.
Cost Picture
| Cost Metric | Value |
|---|---|
| Server cost | £0.50/hr (£99/mo) |
| Cost per 1M tokens | £5.885 |
| Tokens per £1 | 169,924 |
| Break-even vs API | ~1 req/day |
The per-token rate of £5.89/M is slightly higher than the RTX 4060 (£5.26/M), reflecting the card’s higher monthly cost. However, the 4060 Ti’s advantage lies in what the extra VRAM unlocks: longer contexts, multi-user serving, and more stable sustained throughput. Batching at bs=8 brings effective cost to around £3.68/M. Use the benchmark comparison tool to see where each GPU sits.
Our Take
The RTX 4060 Ti hits a practical middle ground for Gemma 2 9B. You get interactive-speed inference with enough memory headroom to actually use the model’s 8K context window. For staging environments, internal chatbots, and moderate-traffic applications, it delivers without overspending. When you need full FP16 precision or higher concurrency, the RTX 3090 is the natural step up.
Quick deploy:
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 99Deep dive in the Gemma hosting guide. Related reads: best GPU for LLM inference, cheapest GPU for AI, benchmark index.
Gemma 2 9B with 8K Context — RTX 4060 Ti
Enough VRAM to run properly. UK datacentre, flat monthly pricing, root access.
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