The RTX 5070 Ti hit at $749 MSRP with 16GB GDDR7 and 896 GB/s of memory bandwidth. That bandwidth number is the one to pay attention to. For local LLM inference, tokens-per-second is almost entirely determined by how fast you can move model weights from VRAM through the memory bus — and the 5070 Ti's 896 GB/s is a 78% jump over the RTX 4070 Ti Super's 504 GB/s.
That's not a marginal improvement. That's a generation-over-generation leap that lands at a price point most people can actually reach.
Specs That Matter for LLM Inference
Here's the 5070 Ti's relevant hardware profile:
- VRAM: 16GB GDDR7
- Memory bus: 256-bit
- Bandwidth: 896 GB/s
- CUDA cores: 8,960
- TDP: 285–300W (peaks around 350W under sustained load)
- Architecture: Blackwell GB203-300
- MSRP: $749
The 256-bit bus width combined with GDDR7's 28 Gbps speed per pin gets you that 896 GB/s figure. For context: the RTX 4090 has 1,008 GB/s on a 384-bit bus. The 5070 Ti is close. Not identical, but genuinely close — and it's $1,050 cheaper at MSRP.
Note
Memory bandwidth is the primary bottleneck for token generation. CUDA cores and compute TFLOPS matter for prompt processing (prefill), but once you hit the generation phase — where the model outputs tokens one by one — you're waiting on memory throughput, not math. This is why the 5070 Ti's bandwidth number matters more than its CUDA core count.
Real Inference Numbers
LocalScore benchmarks on the RTX 5070 Ti show:
- Llama 3.2 1B Q4: 9,213 t/s prompt, 88.4 t/s generation
- Llama 3.1 8B Q4: 2,473 t/s prompt, 36.8 t/s generation
- Qwen 2.5 14B Q4: 1,740 t/s prompt, 28.3 t/s generation
For reference: the RTX 4090 pulls around 42 t/s on 8B generation and 32 t/s on 14B. The 5070 Ti is within 12-15% on generation speed, at less than half the current street price of a 4090.
Prompt processing speed (prefill) is where you feel the most benefit for large context windows — feeding in a 50,000-token document before asking the model to summarize it. The 5070 Ti's prefill speed is strong enough that you won't be sitting around watching a progress bar.
VRAM: 16GB and What It Fits
16GB at Q4_K_M quantization gets you:
- 7B models: comfortable with context left over
- 14B models: fits well, around 10GB used at Q4
- 24B-27B models (Gemma 3, Mistral Small 3.1): tight but possible at Q4 with smaller context
- 32B models: needs Q2/Q3, quality hits are real
- 70B models: not happening on a single card. You'd need CPU offloading, which kills speed.
16GB is not a limitation for most workloads. The 24GB cards (RTX 3090, 4090) give you more headroom with 30B+ models at higher quantization, but the people who need that are already buying those cards. For anyone running 7B-14B models — which is most people — 16GB is exactly right.
Tip
If you're choosing between the RTX 5070 Ti and a used RTX 4090 purely for local AI, run the math on your target model size. For 7B-14B work, the 5070 Ti's bandwidth advantage over a used 3090 (568 GB/s) means ~30-40% faster generation. For 24B+ models at high quantization, the 3090's 24GB VRAM wins even if it's slower per token.
GDDR7 and Blackwell Features
The 5070 Ti supports NVIDIA's NVFP4 quantization format, exclusive to Blackwell. Think of NVFP4 as a hardware-native 4-bit quantization that the GPU processes with dedicated silicon rather than emulating it. Early results suggest NVFP4 can match W8A8 quality at W4A8 memory usage — meaningful for fitting larger models into 16GB.
Right now, framework support for NVFP4 is limited to vLLM and a handful of specialized inference runtimes. Ollama doesn't support it yet. But it's coming, and it's worth noting as a reason the 5070 Ti will age well — software improvements will extract more from this hardware over time.
SK Hynix GDDR7 modules on some variants have headroom for overclocking to around 1,088 GB/s effective bandwidth. Whether that matters in practice depends on your inference software, but it's ceiling that didn't exist with GDDR6.
How It Compares to the Other Cards You're Considering
Let's be direct about the comparisons people are actually making:
RTX 5070 Ti vs RTX 4090 (used, ~$2,200)
The 4090 has 24GB VRAM and 1,008 GB/s bandwidth. It's meaningfully better for 30B+ models. But at $2,200 used vs $749 MSRP, you're paying a 194% premium for roughly 15-20% faster generation on comparable model sizes and more VRAM you may not need. If you run 70B models regularly, the 4090 is worth it. Otherwise, the 5070 Ti is the smarter buy.
RTX 5070 Ti vs RTX 5080 (~$999)
The 5080 has 16GB GDDR7 too, but higher bandwidth (960 GB/s) and 10,752 CUDA cores. The real performance delta for LLM inference is maybe 8-12% in generation speed. At $250 more MSRP — and significantly more at street price — that gap doesn't justify the premium for most workloads. The 5070 Ti is the better value.
RTX 5070 Ti vs RTX 4070 Ti Super (used, ~$600)
The 5070 Ti's 896 GB/s vs the 4070 Ti Super's 672 GB/s is a real 33% bandwidth lead. Same VRAM (16GB). The used 4070 Ti Super at $600 is reasonable if you find one in good condition. But paying $150 more for the 5070 Ti gets you a full generation of architectural improvements, better quantization support, and a new warranty. At $749 MSRP — not street price — that's a real trade-off worth considering.
Caution
Street prices for the RTX 5070 Ti are running above MSRP in early March 2026. Cards showing up around $800-$950 are the norm right now, not $749. If you can find one at or near MSRP, buy it. If you're looking at $900+, a used RTX 3090 or 4090 might be worth comparing. Check our GPU price tracker for current market prices.
Power Draw Considerations
285-300W TDP means a quality PSU matters. NVIDIA recommends at least an 800W unit for a system built around the 5070 Ti, but 750W is fine if the rest of your build is modest (a mid-range CPU, no other power-hungry components).
For 24/7 inference — where the card is pulling near-TDP continuously — thermal stability is worth thinking about. The 5070 Ti runs cooler than the 5090 (450W TDP) but sustained 300W loads require adequate case airflow. A card idling at 80°C during inference that spikes to 88°C during heavy prompt processing isn't a problem. A card sitting at 87°C at idle is.
The ATX 3.1 PSU connector (one 16-pin) is the new standard. If your PSU uses an adapter, make sure it's rated for the sustained load, not just peak.
Who Should Buy This Card
The RTX 5070 Ti makes sense if you're building a new local LLM rig and your primary use is 7B-24B models. At MSRP, it hits a genuine performance-per-dollar sweet spot that hasn't existed for GPU builders since before supply chains went sideways.
It does not make sense if you regularly run 70B models and need a single-card solution — go 4090 or 3090. It also doesn't make sense at $900+ street prices when a used 4090 starts looking competitive.
Wait for prices to normalize if you can. Buy at or near $749 if you find one. For the full picture on which GPU makes sense for your budget and model targets, our GPU comparison guide covers every relevant tier. And if you're deciding between this and a higher-end card, the 5090 vs 4090 breakdown explains exactly where the premium is justified.
Prices and availability reflect March 2026 market conditions. CraftRigs may earn a commission from qualifying purchases through affiliate links.