Running a 9-billion-parameter model on a 6 GB GPU sounds like a recipe for frustration. In practice, Google’s Gemma 2 9B does squeeze onto the RTX 3050 — barely — and still delivers usable inference speeds. We tested the limits on a GigaGPU dedicated server.
What 8.4 Tokens per Second Feels Like
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 8.4 tok/s |
| Tokens/sec (batched, bs=8) | 10.9 tok/s |
| Per-token latency | 119.0 ms |
| Precision | INT4 |
| Quantisation | 4-bit GGUF Q4_K_M |
| Max context length | 4K |
| Performance rating | Acceptable |
Benchmark: 512-token prompt, 256-token completion, single-stream, llama.cpp with Q4_K_M GGUF. At 8.4 tok/s, responses trickle in rather than stream — think two to three words per second. Good enough for development, but you will feel the wait in interactive sessions.
The VRAM Squeeze
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 6.4 GB |
| KV cache + runtime | ~1.0 GB |
| Total RTX 3050 VRAM | 6 GB |
| Free headroom | ~0.0 GB |
There is effectively zero headroom. The model itself exceeds the 6 GB card, so llama.cpp spills some layers to system RAM via partial offloading. Keep context short (4K max) and avoid concurrent requests or you will hit out-of-memory crashes. This is a single-stream, short-context-only configuration.
Cost at This Tier
| Cost Metric | Value |
|---|---|
| Server cost | £0.25/hr (£49/mo) |
| Cost per 1M tokens | £8.267 |
| Tokens per £1 | 120,963 |
| Break-even vs API | ~1 req/day |
At £8.27 per million tokens, the 3050 is not the most efficient way to run Gemma 2 9B — the higher-end cards achieve much lower per-token costs. Batching helps a little, bringing the rate down to around £5.17/M. Still, at £49/mo, the entry price is hard to beat if your budget is tight. See our tok/s benchmark tool to weigh your options.
Should You Bother?
The RTX 3050 makes sense for Gemma 2 9B in exactly two scenarios: local development where you need to validate prompts and outputs before deploying to a bigger GPU, and very low-volume internal tasks where latency does not matter. For anything resembling production, step up to at least the RTX 4060 Ti for a much smoother experience.
Get started:
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 99Full walkthrough in the Gemma hosting guide. Related: best GPU for LLM inference, cheapest GPUs for AI, and all benchmark results.
Try Gemma 2 9B on the RTX 3050
Lowest-cost entry point for 9B inference. UK datacentre, root access, £49/mo flat.
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