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Lambda Labs is the de-facto choice for serious ML training — H100 SXM clusters, reserved capacity, Linux-friendly tooling. They also offer RTX 4090 instances. GigaGPU offers the same RTX 4090 hardware as a dedicated bare-metal rental. Where do they compete and where do they not?
For RTX 4090-class inference, GigaGPU dedicated (£289/mo) is dramatically cheaper than Lambda's on-demand 4090s. For H100 cluster training, Lambda wins — we don't stock H100 in the dedicated catalogue. For dev / Jupyter notebooks, both work; pick by region.
What each one offers
- GigaGPU: Bare-metal dedicated RTX 4090 in UK. Monthly fixed price. Full root. SSH only. Single-tenant.
- Lambda: VM-based RTX 4090 (and H100 / GH200) in US datacenters. Hourly pricing. Linux + ML tooling pre-installed. Multi-tenant underneath but each VM is dedicated GPU.
Price comparison
| Card | GigaGPU monthly | Lambda on-demand | Lambda reserved (1 yr) |
|---|---|---|---|
| RTX 4090 24 GB | £289 | $0.50/hr (~£295/mo at 24/7) | ~£200/mo |
| A100 40 GB | POA | $1.10/hr | ~£540/mo |
| A100 80 GB | POA | $1.99/hr | ~£950/mo |
| H100 SXM5 80 GB | POA | $3.49/hr | ~£1,650/mo |
Lambda’s on-demand 4090 at $0.50/hr is roughly tied with GigaGPU dedicated at full utilisation. Below 24/7 utilisation, GigaGPU is cheaper. Lambda’s 1-year reserved RTX 4090 is the cheapest of the three for steady workloads, but requires a 12-month commitment.
By workload
| Workload | Best choice | Why |
|---|---|---|
| UK / EU inference | GigaGPU | Data residency + cheaper |
| US-based inference | Lambda Reserved | Region locality |
| H100 / cluster training | Lambda | GigaGPU does not stock H100 |
| Long fine-tuning, single GPU | GigaGPU dedicated | Predictable monthly bill |
| Notebook / exploration | Either; Lambda has nicer UX | — |
| Multi-GPU 4× / 8× clusters | GigaGPU clusters or Lambda | Both options |
Verdict
For RTX 4090 inference in UK/EU, GigaGPU dedicated wins. For H100 training, Lambda wins. For multi-GPU build-out, both have credible offerings — pick by region and reservation flexibility.
Bottom line
Lambda is the right answer for serious training and US-located workloads; GigaGPU is the right answer for UK-resident inference and steady workloads at lower price points. Both can co-exist in a hybrid architecture. See SageMaker alternatives for the broader landscape.
