Rent the RTX 4090 if…
your workload fits in 24 GB — SDXL, FLUX, small LLMs, LoRA training, CUDA development — and you want the best value per month.
Deploy an RTX 4090 · $189/moQuestions? Open a ticket from your console — an engineer replies within one business day.
$189 against $1,249 a month: the H100 SXM has 3.3× the memory, 3.3× the bandwidth and NVLink; the RTX 4090 has the lowest price for a GPU this fast.
The short answer
These two rarely compete for the same job. The RTX 4090 is the cost-effective choice for work that fits in 24 GB — image generation, 7B–13B models, LoRA training, development — at about a sixth of the H100's price. The H100 SXM is for the jobs a 4090 cannot do: training and fine-tuning large models, 70B-class inference, FP8 at scale and 8-GPU NVLink nodes. If a 4090 can hold your job, the H100 is hard to justify; if it cannot, splitting the job across several 4090s over PCIe is rarely as fast as one H100 with 80 GB.
your workload fits in 24 GB — SDXL, FLUX, small LLMs, LoRA training, CUDA development — and you want the best value per month.
Deploy an RTX 4090 · $189/moyou train or fine-tune large models, serve 70B-class models, need FP8 at scale or an 8-GPU NVLink node.
Deploy an H100 · $1,249/moSpecifications
NVIDIA's published figures for each part, tensor throughput without sparsity. The last column is how the RTX 4090 compares, as a multiple of the H100.
| Specification | NVIDIA RTX 4090 | NVIDIA H100 SXM | RTX 4090 vs H100 |
|---|---|---|---|
| Architecture | Ada Lovelace · 2022 | Hopper · 2022 | — |
| GPU memory | 24 GB GDDR6X | 80 GB HBM3 | 0.3× |
| Memory bandwidth | 1 TB/s | 3.35 TB/s | 0.3× |
| FP16 / BF16 tensor (dense) | 165 TFLOPS (330 with FP16 accumulate) | ~989 TFLOPS | 0.17× |
| FP8 tensor (dense) | 330 TFLOPS (661 with FP16 accumulate) | ~1,979 TFLOPS | 0.17× |
| FP4 tensor (dense) | Not supported | Not supported | — |
| Maximum power | 450 W | Up to 700 W | 0.64× |
| Form factor | PCIe Gen4 x16 | SXM5 | — |
| GPU-to-GPU link | PCIe only | NVLink 4 · 900 GB/s | — |
| Multi-Instance GPU (MIG) | No | Up to 7 instances | — |
| ECC memory | No | Yes | — |
| Video engines | 2 NVENC · 1 NVDEC (AV1) | 7 NVDEC · 7 NVJPG | — |
| CUDA compute capability | 8.9 | 9.0 | — |
| Dedicated host here | 8 vCPU · 64 GB RAM · 1 TB NVMe | 24 vCPU · 256 GB RAM · 3 TB NVMe | — |
| Price per month here | $189 | $1,249 | 0.15× |
Sources: NVIDIA datasheets and product pages for each GPU. Tensor figures are dense (NVIDIA often prints them doubled “with sparsity”). Prices are this site's flat monthly rates for a dedicated server with the host listed, in USD excluding VAT.
Value
Price per month divided into the two resources most jobs run out of first — memory and memory bandwidth — and what the same GPU costs kept on for a month at the market-median hourly rate.
Model sizes
The largest model whose weights fit on a single GPU, keeping 10% of the memory for the runtime, activations and a small KV cache. Long contexts and large batches need more — size them with the GPU memory calculator.
| Weights precision | NVIDIA RTX 4090 | NVIDIA H100 SXM | Typical use |
|---|---|---|---|
| 16-bit (BF16 / FP16) | up to ~11B parameters | up to ~36B parameters | Full-precision inference; the base weights of a LoRA fine-tune |
| 8-bit (FP8 / INT8) | up to ~22B parameters | up to ~72B parameters | FP8 serving with vLLM or TensorRT-LLM on GPUs that support it; INT8 weights elsewhere |
| 4-bit (GPTQ, AWQ, Q4) | up to ~39B parameters | up to ~130B parameters | Quantised inference on a budget; QLoRA fine-tuning |
Parameters in billions, weights only: 2 bytes per parameter at 16-bit, 1 at FP8 or INT8, about 0.55 at 4-bit (GPTQ, AWQ, GGUF Q4). Mixture-of-experts models need memory for all their experts, not just the active ones.
Prices
Per dedicated GPU server, in USD excluding VAT — CPU cores, RAM, NVMe, 20 TB of transfer and 24/7 support included.
| Term | NVIDIA RTX 4090 | NVIDIA H100 SXM | Difference per month |
|---|---|---|---|
| Monthly | $189 | $1,249 | $1,060 |
| 3 months · −5% | $180 | $1,187 | $1,007 |
| 6 months · −10% | $170 | $1,124 | $954 |
| 12 months · −15% | $161 | $1,062 | $901 |
| Dedicated host | 8 vCPU · 64 GB RAM · 1 TB NVMe · 10 Gbps | 24 vCPU · 256 GB RAM · 3 TB NVMe · 25 Gbps | included |
For jobs that fit in 24 GB, often — at a fraction of the price. For large-model training, 70B-class inference or anything that needs NVLink, no: the H100 has 3.3× the memory and bandwidth plus data center features the 4090 lacks.
For 7B–13B models, yes: an 8B model fits at FP8 or 16-bit with room for its KV cache, and 30B-class models fit at 4-bit. Larger models need more than 24 GB.
The RTX 4090, on price: SDXL and FLUX fit in 24 GB and run fast on Ada tensor cores. The H100 is faster per image but costs 6.6× as much per month.
19 of 36 RTX 4090 units were free at the last stock update; 14 of 64 H100 units were free at the last stock update (18 September 2026). Both are provisioned automatically, in under 10 minutes after payment.
Yes. Both are rented with an email address and paid from a prepaid balance in Bitcoin, Ether or USDT — $189 and $1,249 a month, dedicated, with no identity check and no card.
Dedicated, single-tenant and live in under 10 minutes: the RTX 4090 at $189 or the H100 at $1,249 a month, paid in BTC, ETH or USDT with no KYC.