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Comparison

T4 vs RTX 4000 Ada

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.

T4
$0.04/h16 GB · Turing · 96 available
RTX 4000 Ada
$0.06/h20 GB · Ada Lovelace · 32 available
Price gap
33%On spot, per GPU-hour
Over 100 h
$2.00Difference 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 4000 Ada

20 GB against 16 GB. At FP16 that is about 8B parameters on one GPU, against 6B.

The T4 costs 33% less per hour

$0.04 against $0.06 on spot — $2.00 of difference over 100 GPU-hours.

Token generation is faster on the RTX 4000 Ada

360 GB/s against 320 GB/s. Inference reads the whole model from memory for every token, so bandwidth, not FLOPS, usually sets the ceiling.

More memory per dollar on the T4

400 GB per dollar per hour against 333 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. T4 leads on 5 rows, RTX 4000 Ada on 5.

  T4 RTX 4000 Ada
GPU memoryDecides which models fit at all 16 GB GDDR6 20 GB GDDR6
Memory bandwidthSets token throughput on inference 320 GB/s 360 GB/s
FP32 8 TFLOPS 27 TFLOPS
8-bit float (FP8)Halves the memory a model needs, where the stack supports it Not supported Supported
Architecture Turing (2018) Ada Lovelace (2023)
Board powerLower is cheaper to run at scale — for us, not for your bill 70 W 130 W
GPU-to-GPU linkOnly matters for multi-GPU training PCIe only PCIe only
Node sizes 1×, 2×, 4× 1×, 2×, 4×
Spot price $0.04/h $0.06/h
On-demand price $0.07/h $0.09/h
Memory per dollarGPU memory per $1 of spot time, per hour 400 GB 333 GB
30-day reclaim rateShare of spot instances reclaimed <5% <5%
Available now 96 GPUs 32 GPUs

Specifications come from each vendor's datasheet — linked on the T4 page and the RTX 4000 Ada 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 T4 16 GB RTX 4000 Ada 20 GB
Llama 3.1 8B8B parameters FP16 · 16 GB Needs 2× node Fits
INT4 · 4 GB Fits Fits
Qwen2.5 32B32B parameters FP16 · 64 GB Needs more than one node Needs 4× node
INT4 · 16 GB Needs 2× node Fits
Llama 3.3 70B70B parameters FP16 · 140 GB Needs more than one node Needs more than one node
INT4 · 35 GB Needs 4× node Needs 4× node
Mixtral 8x22B141B parameters FP16 · 282 GB Needs more than one node Needs more than one node
INT4 · 71 GB Needs more than one 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.

 T4RTX 4000 Ada
One hour, spot$0.04$0.06
One day, spot$0.96$1.44
100 GPU-hours, spot$4.00$6.00
100 GPU-hours, on-demand$7.00$9.00
A month, reserved$36.50$43.80

Take the T4 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 43% below its own on-demand price, with a 2-minute notice before a reclaim.

Full T4 page

Take the RTX 4000 Ada 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 4000 Ada page

FAQ

T4 or RTX 4000 Ada?

Which is cheaper, the T4 or the RTX 4000 Ada?

The T4, at $0.04 per GPU-hour on spot against $0.06 — 33% less. On-demand: $0.07 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 4000 Ada on capacity (20 GB) and the RTX 4000 Ada 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 $0.10 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.