RTX 3050 - Order Now
Home / Blog / GPU Comparisons / Blackwell vs Ada – The Generational Leap for AI Workloads
GPU Comparisons

Blackwell vs Ada – The Generational Leap for AI Workloads

What actually changed between RTX 40-series Ada and RTX 50-series Blackwell for AI, in plain terms, without marketing noise.

Nvidia positions Blackwell as the first mass-market architecture built for AI. That is partially marketing. It is also partially true. Between RTX 4060 Ti (Ada Lovelace) and RTX 5080 (Blackwell) on our dedicated hosting, the differences are concrete. Here is what matters.

Topics

FP8 Tensor Cores

Ada tensor cores did FP16 and FP32 well but did not natively accelerate FP8. Blackwell adds first-class FP8 support. This matters because FP8 models are roughly half the size of FP16 while keeping most of the quality. On a Blackwell card you can load a 7B FP8 model where an 8 GB Ada card would have to drop to INT4. The token speed advantage is real because FP8 is the native format, not a conversion.

GDDR7

Every consumer Blackwell card uses GDDR7. The practical effect: bandwidth per GB of VRAM jumps by 50-70% over the GDDR6 that Ada used. The 5080 hits ~960 GB/s where the 4070 capped around ~504 GB/s. For memory-bound LLM decode, this is the bigger real-world upgrade than FP8 in most workloads.

DP4A Successors

Blackwell extends Nvidia’s integer acceleration paths. INT4 operations run faster on native hardware, which benefits GGUF/GPTQ/AWQ quantised models. On Ada, INT4 inference often converts internally to INT8 or uses slower paths. Blackwell’s DP4A improvements and related tensor instructions run INT4 closer to its theoretical speed. See our quantisation comparison for how this plays out.

What It Adds Up To

For a typical 7B LLM at INT8, a Blackwell 5080 delivers roughly 70-85% more tokens/sec than an Ada 4060 Ti despite similar VRAM. The bulk of that gain is bandwidth. For a 7B FP8 model the delta is wider – nearly double – because Ada cannot use FP8 natively. For SDXL image generation the gap is smaller (~30-40%) because image pipelines are more compute-bound.

WorkloadAda (4060 Ti)Blackwell (5080)
Mistral 7B INT8 decode~45 t/s~85 t/s
Mistral 7B FP8 decodeUnsupported natively~110 t/s
SDXL 1024 30 steps~3.5 s~2.3 s

Blackwell Hosting, UK-Based

5060, 5080, and 5090 available on dedicated servers with fixed monthly pricing.

Browse GPU Servers

When Ada Is Still Fine

Ada is not obsolete. The 4060 Ti 16GB at the right monthly price still delivers more VRAM than the 5080. The 4060 is still the value winner at the 8 GB tier when raw decode speed is not critical. If your workload is running steadily on Ada today and your pain is not tokens/sec, you do not need to upgrade. If FP8 checkpoints appear on your roadmap, the next server you spec should be Blackwell.

See the head-to-heads: 4060 vs 5060 and 4060 Ti vs 5060.

Need a Dedicated GPU Server?

Deploy from RTX 3050 to RTX 5090. Full root access, NVMe storage, 1Gbps — UK datacenter.

Browse GPU Servers

admin

We benchmark, deploy, and optimise GPU infrastructure for AI workloads. All data in our guides comes from real-world testing on our UK-based dedicated GPU servers.

Ready to deploy your AI workload?

Dedicated GPU servers from our UK datacenter. NVMe storage, 1Gbps networking, full root access.

Browse GPU Servers Contact Sales

Have a question? Need help?