RTX 5070 Ti vs RTX 5080 for local AI in 2026
16 GB Blackwell upper-mid; the new 'value Blackwell' tier.
- VRAM
- 16 GB
- Bandwidth
- 896 GB/s
- TDP
- 300 W
- Price
- $750-900 (2026 retail)
16 GB GDDR7 Blackwell; the second-tier 2026 consumer card.
- VRAM
- 16 GB
- Bandwidth
- 960 GB/s
- TDP
- 360 W
- Price
- $1,000-1,300 (2026 retail; supply variable)
RTX 5070 Ti
16 GB Blackwell upper-mid; the new 'value Blackwell' tier.
RTX 5080
16 GB GDDR7 Blackwell; the second-tier 2026 consumer card.
Both 16 GB GDDR7. Same Blackwell architecture. The 5080 has higher CUDA core count, faster memory clocks (960 GB/s vs ~896 GB/s on the 5070 Ti), and a slightly higher TDP (360W vs 300W). The price gap is $250 retail.
For local AI specifically, the 16 GB VRAM ceiling is identical — both cards run the same models with the same context limits. The 5080's edge is on prefill speed (compute-bound) and decode at the high end of bandwidth utilization. The 5070 Ti's edge is power efficiency + lower system requirements.
This is the cleanest 'is the bandwidth premium worth it' decision in the Blackwell lineup. The honest answer for most buyers: no, the 5070 Ti is the value pick. Save the $250.
Who wins each workload
Each row is a workload local-AI operators actually run. Verdicts derived from VRAM math + bandwidth — no editorial hand-wave.
What each card actually runs
VRAM math against a canonical set of popular models. The largest context window that fits with headroom appears in each cell.
| Model | RTX 5070 Ti | RTX 5080 |
|---|---|---|
Qwen 3 14B Q4_K_M 14B params · Q4_K_M | 16K ctx, tight | 16K ctx, tight |
Qwen 3 32B Q4_K_M 32B params · Q4_K_M | OOM | OOM |
Llama 3.3 70B Q4_K_M 70B params · Q4_K_M | OOM | OOM |
DeepSeek R1 distill 32B 32B params · Q4_K_M | OOM | OOM |
Mixtral 8x22B Q4 141B params · Q4_K_M | OOM | OOM |
FLUX.1 image gen 12B params · FP16 | OOM | OOM |
Llama 3.3 70B Q4_K_M
Computed from each option's sustained TDP × predicted tok/s at $0.16/kWh. Cloud baseline: Claude Sonnet 4.6 (input + output).
Electricity-only cost — excludes the upfront hardware purchase, cooling, and amortized component depreciation. Hardware ROI math lives at /cost-vs-cloud; this line is for "is the marginal token cheaper than Claude?" not "should I buy this rig instead of paying Anthropic." MODELED ESTIMATE.
Quick decision rules
Operational matrix
| Dimension | RTX 5070 Ti 16 GB Blackwell upper-mid; the new 'value Blackwell' tier. | RTX 5080 16 GB GDDR7 Blackwell; the second-tier 2026 consumer card. |
|---|---|---|
VRAM Identical; the workload ceiling is the same. | Limited 16 GB GDDR7. 13-32B Q4 comfortable; 70B Q4 short-context only. | Limited 16 GB GDDR7. Same as 5070 Ti. |
Memory bandwidth Decode speed. | Strong 896 GB/s. ~7% lower than 5080. | Strong 960 GB/s. Modest advantage, ~7% faster decode. |
Compute (FP16/FP8) Prefill + image-gen workload throughput. | Strong ~78 TFLOPS FP16. Solid mid-range Blackwell. | Excellent ~98 TFLOPS FP16. ~25% faster prefill on 8K+ prompts. |
Power draw Sustained-load wall power. | Strong 300W TDP. 750W PSU sufficient. | Acceptable 360W TDP. 850W PSU recommended. |
Price (2026) Acquisition cost. | Strong $750-900 retail. | Acceptable $1,000-1,300 retail. |
Resale value (2-3 yr) Predicted % of purchase price held. | Strong ~50-60% expected. | Strong ~55-65% expected — flagship-adjacent holds slightly better. |
Tiers are qualitative editorial labels, not derived from a single benchmark. For tok/s and VRAM measurements on these cards, browse the corpus or request a benchmark.
Who should AVOID each option
Avoid the RTX 5070 Ti
- If image generation (compute-bound) is your daily workload
- If long-prompt agent workflows drive prefill bottlenecks
- If you'll resell in 2 years and want the marginally better $-recovery
Avoid the RTX 5080
- If your daily workload is 13-32B Q4 inference (5070 Ti is identical)
- If power budget is constrained (300W vs 360W matters)
- If you'd rather spend the $250 on RAM / SSD / better PSU
Workload fit
RTX 5070 Ti fits
- 13-32B Q4 inference
- Cost-conscious Blackwell entry
- Power-efficient single-card builds
RTX 5080 fits
- Image generation (compute-bound)
- Long-prompt agent workflows
- Prefill-heavy production serving
Reality check
The 5080's 7% bandwidth advantage is mostly invisible on quantized inference at typical context. You'll see the difference on FP16 inference (compute-bound) and on prefill of long prompts.
If you're already accepting the 16 GB VRAM ceiling, you're already accepting the same workload limit. Spending $250 more for marginal speed inside that ceiling is rarely the right call.
Both cards face the same workload-dependent bottleneck — at 16 GB you're choosing between 13B Q4 with comfort and 32B Q4 with care. Nothing the 5080 does breaks past that ceiling.
Power, noise, and heat
- 5070 Ti runs comfortably under 290W actual draw. Quieter under sustained inference; runs 65-72°C on AIB designs.
- 5080 hits 350-360W sustained on heavy workloads. AIB cooler quality matters; reference design audibly louder than 5070 Ti.
- Both fit standard ATX cases. Neither is multi-GPU friendly compared to lower-tier cards (3-slot designs typical).
Where to buy
Where to buy RTX 5080
Editorial price range: $1,000-1,300 (2026 retail; supply variable)
Affiliate links — no extra cost. Prices are editorial ranges, not real-time. Click through to verify.
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Editorial verdict
The 5070 Ti is the right call for most buyers. Same 16 GB VRAM, same GDDR7, same Blackwell generation. The $250 saving funds better RAM, a quieter case, or a higher-quality PSU.
Buy the 5080 only if you specifically value the prefill / compute advantage for long-prompt agent workflows or image generation. The bandwidth advantage on quantized inference is too small to justify the premium.
If you're considering either, also look at used 3090 ($700-1,000). Same 24 GB VRAM tier as 5090; outperforms both 5070 Ti and 5080 on the workloads that need >16 GB. Different tradeoff (used silicon, no warranty).
HonestyWhy benchmark numbers on this page might not reflect your real experience
- tok/s is not user experience. Humans read at ~10-15 tok/s — anything above that is buffer time, not perceived speed.
- Context length changes everything. A 70B Q4 model at 1024 tokens generates ~25 tok/s; the same model at 32K context drops to ~8-12 tok/s as KV cache fills.
- Quantization changes the conclusion. Q4_K_M vs Q5_K_M vs Q8 produce different speed AND different quality. A benchmark at one quant doesn't translate to another.
- Thermal throttling changes long sessions. The first 15 minutes of a benchmark see boost-clock peak; the next 4 hours see steady-state, which is 5-15% slower depending on case airflow.
- Driver and runtime versions silently shift winners. A 2024 benchmark on PyTorch 2.4 + CUDA 12.4 doesn't reflect 2026 reality on PyTorch 2.6 + CUDA 12.6. Discount benchmarks older than 6 months.
- Vendor and YouTuber benchmarks are cherry-picked. The standard 'Llama 3.1 70B Q4 at 1024 tokens' chart shows peak decode on a tiny prompt — exactly the conditions least representative of daily use.
- A 25-30% throughput gap between two cards rarely translates to a 25-30% experience gap. Both cards are fast enough; the differentiator is usually VRAM ceiling, not raw decode speed.
We try to surface these caveats where they apply. If a number on this page reads more confident than it should, please email us via contact. See also our methodology and editorial philosophy.
Don't see your specific workload?
The matrix above is editorial. If you want a measured tok/s number for a specific model + quant on either card, file a benchmark request — the community claims requests and reproduces them under our methodology checklist.