New B200 spot capacity is live in US East from $1.69 per GPU-hour. See availability
Comparison

RTX 4090 vs RTX 4070 Ti SUPER

Both run on the same nodes, the same templates and the same tiers here — so the choice comes down to memory, bandwidth and price. Every figure below is from the catalogue or the vendor datasheet.

RTX 4090
$0.06/h24 GB · Ada Lovelace · 128 available
RTX 4070 Ti SUPER
$0.06/h16 GB · Ada Lovelace · 24 available
Price gap
NoneOn spot, per GPU-hour
Over 100 h
$0.00Difference for the same GPU-hours
In short

What actually separates them.

Four differences that change a decision, computed from the numbers rather than asserted.

Bigger models fit on the RTX 4090

24 GB against 16 GB. At FP16 that is about 9B parameters on one GPU, against 6B.

More memory per dollar on the RTX 4090

400 GB per dollar per hour against 267 GB. The usual tie-breaker when both cards fit the job.

Side by side

Every figure we hold on both cards.

A row is marked only when both values exist and one is genuinely better. RTX 4090 leads on 4 rows, RTX 4070 Ti SUPER on 2.

  RTX 4090 RTX 4070 Ti SUPER
GPU memoryDecides which models fit at all 24 GB GDDR6X 16 GB GDDR6X
FP32 83 TFLOPS 44 TFLOPS
8-bit float (FP8)Halves the memory a model needs, where the stack supports it Supported Supported
Architecture Ada Lovelace (2022) Ada Lovelace (2024)
Board powerLower is cheaper to run at scale — for us, not for your bill 450 W 285 W
GPU-to-GPU linkOnly matters for multi-GPU training PCIe only PCIe only
Node sizes 1×, 2×, 4× 1×, 2×, 4×
Spot price $0.06/h $0.06/h
On-demand price $0.09/h $0.09/h
Memory per dollarGPU memory per $1 of spot time, per hour 400 GB 267 GB
30-day reclaim rateShare of spot instances reclaimed 10–15% 10–15%
Available now 128 GPUs 24 GPUs

Specifications come from each vendor's datasheet — linked on the RTX 4090 page and the RTX 4070 Ti SUPER page. Prices are ours, per GPU-hour, billed per minute.

What fits

The same models, on one GPU of each.

Weights only, with 20% of the memory kept free for activations and the KV cache. A cross means one GPU is not enough — the model page has the multi-GPU arithmetic.

Model Precision RTX 4090 24 GB RTX 4070 Ti SUPER 16 GB
Llama 3.1 8B8B parameters FP16 · 16 GB Fits Needs 2× node
FP8 · 8 GB Fits Fits
INT4 · 4 GB Fits Fits
Qwen2.5 32B32B parameters FP16 · 64 GB Needs 4× node Needs more than one node
FP8 · 32 GB Needs 2× node Needs 4× node
INT4 · 16 GB Fits Needs 2× node
Llama 3.3 70B70B parameters FP16 · 140 GB Needs more than one node Needs more than one node
FP8 · 70 GB Needs 4× node Needs more than one node
INT4 · 35 GB Needs 2× node Needs 4× node
Mixtral 8x22B141B parameters FP16 · 282 GB Needs more than one node Needs more than one node
FP8 · 141 GB Needs more than one node Needs more than one node
INT4 · 71 GB Needs 4× node Needs more than one node
Cost

The same 100 GPU-hours on each.

Compute only, billed per minute. Storage is $0.08 per GB-month on either card.

 RTX 4090RTX 4070 Ti SUPER
One hour, spot$0.06$0.06
One day, spot$1.44$1.44
100 GPU-hours, spot$6.00$6.00
100 GPU-hours, on-demand$9.00$9.00
A month, reserved$43.80$43.80

Take the RTX 4090 when…

  • Fine-tuning small models — LoRA on 7B–13B models, and full fine-tuning below 3B.
  • Image and video generation — Diffusion pipelines run entirely in memory at this capacity.
  • Anything that can checkpoint — On the spot tier this model is 33% below its own on-demand price, with a 2-minute notice before a reclaim.

Full RTX 4090 page

Take the RTX 4070 Ti SUPER when…

  • Quantised inference — Small and medium models in 4-bit or 8-bit form, with the whole model resident in memory.
  • Computer vision and batch jobs — Detection, classification, embeddings and transcoding at a very low hourly rate.
  • Anything that can checkpoint — On the spot tier this model is 33% below its own on-demand price, with a 2-minute notice before a reclaim.

Full RTX 4070 Ti SUPER page

FAQ

RTX 4090 or RTX 4070 Ti SUPER?

Which is cheaper, the RTX 4090 or the RTX 4070 Ti SUPER?

The RTX 4070 Ti SUPER, at $0.06 per GPU-hour on spot against $0.06 — 0% less. On-demand: $0.09 against $0.09.

Which one should I take for inference?

Take the one that holds the model with room for the KV cache, then the one with more memory bandwidth. Here that is the RTX 4090 on capacity (24 GB) and the RTX 4090 on bandwidth.

Can I run both on the same job?

Not inside one instance — a node holds GPUs of a single model. You can run two instances in parallel, on the same persistent volume in turn, or split the work: a big card for the model that needs capacity, a cheap one for the parts that do not.

Other comparisons: the full list · all 47 models

Get started

Try both for less than the price of deciding.

An hour on each costs $0.12 on spot. Nothing renews, and your disk moves between them.

Billed per minute from the moment the instance is reachable. Minimum credit $40, no subscription.