Fitting a 70-billion-parameter model onto a single consumer GPU is an exercise in compromise. The RTX 3090 can technically run Meta’s LLaMA 3 70B at 4-bit quantisation, but “can run” and “should deploy” are different conversations. We benchmarked it on GigaGPU dedicated hardware to set realistic expectations.
The Reality: 5.2 Tokens per Second
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 5.2 tok/s |
| Tokens/sec (batched, bs=8) | 8.3 tok/s |
| Per-token latency | 192.3 ms |
| Precision | INT4 |
| Quantisation | 4-bit GGUF Q4_K_M |
| Max context length | 4K |
| Performance rating | Marginal |
512-token prompt, 256-token completion, single-stream via llama.cpp Q4_K_M. At 5.2 tok/s, a 200-token response takes nearly 40 seconds. Users will notice the wait.
Why It Is So Tight
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 23 GB |
| KV cache + runtime | ~3.4 GB |
| Total RTX 3090 VRAM | 24 GB |
| Free headroom | ~1.0 GB |
Even at aggressive 4-bit quantisation, LLaMA 3 70B consumes 23 GB of the 3090’s 24 GB. The remaining gigabyte barely covers KV cache for short contexts, forcing llama.cpp to spill the rest to system RAM. Context is capped at 4K, concurrency is impossible, and any VRAM spike risks OOM. This is a single-stream, single-user configuration only.
Cost at This Scale
| Cost Metric | Value |
|---|---|
| Server cost | £0.75/hr (£149/mo) |
| Cost per 1M tokens | £40.064 |
| Tokens per £1 | 24,960 |
| Break-even vs API | ~1 req/day |
At £40/M per million tokens, the per-token cost reflects how slowly the GPU generates output relative to its monthly price. Batching helps somewhat (£25/M at bs=8), but these numbers are dramatically higher than what you would pay running a 7B-8B model on the same card. For context, LLaMA 3 8B on the 3090 achieves £3-4/M. Check the full range in our benchmark comparison tool.
When This Actually Makes Sense
Strictly for experimentation. If you need to evaluate LLaMA 3 70B’s output quality — testing prompts, comparing it against smaller models, running evals — the 3090 lets you do that without renting multi-GPU clusters. Just do not plan a production deployment around these numbers. For production 70B hosting, multi-GPU setups or the RTX 5090 (32 GB) provide a meaningfully better experience.
Test it yourself:
docker run --gpus all -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/llama-3-70b.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99More on large-model hosting in the LLaMA hosting guide. Related: best GPU for LLM inference, cheapest GPU for AI, all benchmarks.
Experiment with LLaMA 3 70B on the RTX 3090
Evaluate 70B output quality on affordable hardware. UK datacentre, root access, £149/mo.
Order RTX 3090
