RTX 5070 Ti vs RTX 5080 for Local AI

1. 30-second verdict

Both cards are Blackwell-generation NVIDIA discrete GPUs with 16 GB GDDR7 on a 256-bit bus and PCIe 5.0 x16 — the same memory class, different compute class. The RTX 5070 Ti (GB203) records 896 GB/s bandwidth, 8,960 CUDA cores, 300 W TDP, launch MSRP $749. The RTX 5080 (GB203) records 960 GB/s bandwidth, 10,752 CUDA cores, 360 W TDP, launch MSRP $999 (NVIDIA manufacturer specs). On hardware-corner.net llama.cpp Qwen3-8B Q4_K_XL (16K, batch 1, 2025-12-09) the 5080 recorded 94.14 tok/s vs 87.54 tok/s — about 7.5% faster — for $250 more at launch MSRP. The honest value split: the 5080 is faster and has 20% more CUDA cores, but the 5070 Ti delivers more tok/s per launch dollar (calculated 0.117 vs 0.094 tok/s per $) and fits exactly the same 16 GB model set. Neither card fits Gemma 3 27B or Qwen 3 32B at Q4_K_M (calculated min 24 GB). Street price is not yet measured on either card; no product_offers rows exist.

2. Memory architecture

Both cards are discrete VRAM, not unified memory. Both record 16 GB GDDR7 on a 256-bit bus: 896 GB/s (RTX 5070 Ti) versus 960 GB/s (RTX 5080) — a 7.1% bandwidth advantage for the 5080 (calculated from recorded values: 960 / 896). This is a same-capacity speed difference, not a capacity gap: both cards load the same models, and both run out of room at the same point. ECC is not recorded on either gpus row. Neither card records NVLink support (nvlink_support is NULL on both, not 0). Two 5070 Tis stay 2×16 GB and two 5080s stay 2×16 GB unless a runtime shards; dual-card tok/s is not yet measured here.

3. Model-fit table

Fit labels use ai_models calculated min/recommended configs plus discrete VRAM from the product records. They are capacity checks, not speed claims.

Because both cards carry 16 GB, the model-fit table is identical for both: 8B-class fits comfortably, 14B-class fits at the calculated minimum, and 24B+ models (Mistral Small 3.2 24B, Gemma 3 27B, Qwen 3 32B) exceed both cards. The 5080's extra compute does not expand which models load — that is purely a speed question, and beyond the sourced rows, speed on those classes is not yet measured on either card.

4. Direct benchmark table

Only non-quarantined benchmarks rows. Same-lab pairs first. A blank cell means no sourced row for that card — not zero. Estimate means is_estimate=1. Do not treat one-sided rows as a pair.

The Qwen3-8B row is the only sourced same-lab pair — 94.14 vs 87.54 tok/s (~7.5% faster 5080), single-workload evidence, not a full performance profile. The 5080 has 10 additional one-sided rows (Ollama Llama-3.1-8B, four UL Procyon text scores, three UL Procyon image scores, SDXL Turbo) with no 5070 Ti counterpart — they are evidence about the 5080, not a comparison. No fine-tune row and no larger-model row exists for either GPU. 24B-class inference speed is not yet measured here (and neither card fits those models anyway).

5. Runtime compatibility

Both are NVIDIA CUDA discrete GPUs on the same Blackwell generation and the same GB203 die. The only sourced stacks in our rows are llama.cpp CUDA 12.8 (both cards, same-lab pair), llama.cpp via Ollama (5080), UL Procyon TensorRT (5080) and ComfyUI (5080). ROCm and MLX do not apply. Both record PCIe 5.0 x16, so host-transfer bandwidth for CPU offload is generation-identical — the 5080's advantage is on-card compute and bandwidth, not the slot. vLLM-style serving beyond the sourced rows is not yet measured on this pair.

6. Quantization compatibility

The sourced same-lab generation row uses Q4_K_XL on Qwen3-8B for both cards — identical quant, identical config, so the 94.14 vs 87.54 tok/s comparison is quant-matched. The 5080-only rows add Q4_K_M (Llama-3.1-8B via Ollama), FP16 and INT8 (UL Procyon image) and TensorRT default precision (Procyon text). Q5/Q6/Q8 decode on this pair is not yet measured. Both cards share the same 16 GB capacity, so quant trade-offs (Q4 vs Q5 vs Q6 on a 14B model) hit both cards identically; the 5080's higher bandwidth gives it more headroom on the same quant, but capacity-limited model choice is unchanged.

7. Max practical context examples

The same-lab llama.cpp pair uses 16K context (Qwen3-8B). Qwen 3 8B native context in our model record is 40,960 tokens; Llama 3.1 8B native context is 131,072 tokens. Whether either GPU holds the full native window at a given quant is not yet measured here. Because VRAM is identical (16 GB), context headroom at the same quant is the same class on both cards; the 5080's extra bandwidth may help decode speed at long context, but no sourced long-context row exists to quantify it at 32K+.

8. Power

Specifications record 300 W TDP (5070 Ti) and 360 W TDP (5080). The 60 W difference is rated board power, not an inference-consumption measurement. Both are single-card, desktop-class power envelopes; the 5080 wants more PSU headroom and typically more cooling. Energy cost per million tokens is not yet measured (no watt-hour rows).

9. Current price

Price class is launch_msrp only. Products table: RTX 5070 Ti $749, RTX 5080 $999, price_checked_at 2026-09-02. street_price_usd is NULL on both. No product_offers rows exist for either SKU. Check live listings rather than treating MSRP as street:

• RTX 5070 Ti current listing (affiliate)
• RTX 5080 current listing (affiliate)
• RTX 5070 Ti product page · RTX 5080 product page

10. Used price

Not yet measured. No used-condition offer rows exist for either SKU. Both launched in 2025, so the used pool is still young. This page will not invent a median. If a used 5080 approaches $749, the extra compute becomes a near-free upgrade; if a used 5070 Ti lands well below MSRP, the value case over the 5080 strengthens.

11. Performance per current dollar

Calculated from the sourced same-lab pair and launch MSRP (both labeled): 87.54 tok/s ÷ $749 = 0.117 tok/s per launch-MSRP dollar (5070 Ti) versus 94.14 tok/s ÷ $999 = 0.094 tok/s per launch-MSRP dollar (5080) — the 5070 Ti delivers roughly 1.24× the tok/s per dollar at launch pricing. The 5080 is faster in absolute terms (~7.5% on the sourced pair) but charges ~33% more MSRP for it. This is a calculated ratio from sourced values, not a stored ranking, and launch MSRP is not street price. No street or offer rows exist to recompute it at current prices.

12. Multi-GPU considerations

Neither card records NVLink support in the gpus table (nvlink_support is NULL on both). Desktop GeForce cards in this class have no NVLink bridge. Two 5070 Tis are 2×16 GB and two 5080s are 2×16 GB, not one pooled pool, unless the runtime shards (tensor parallel / pipeline / expert offload). Dual-card tok/s for this pair is not yet measured. Related: multi-GPU guide, workstations.

13. System requirements

Both: PCIe 5.0 x16 slot, single desktop-class board, 16 GB GDDR7 on 256-bit. 5070 Ti: 300 W TDP. 5080: 360 W TDP (manufacturer spec / vendor spec page). Both are backwards-compatible with PCIe 4.0 motherboards (running at 4.0 speeds). Both fit a standard desktop case; the 5080's 360 W rating wants a bigger PSU margin and typically a pair of power inputs on partner boards. CPU/RAM pairing for these two SKUs is not yet measured on this page.

14. Who should buy the RTX 5070 Ti

• Anyone whose largest model fits in 16 GB at Q4 — Llama 3.1 8B and Qwen 3 8B both list 12 GB as the recommended class, and Qwen3 14B fits at the calculated 16 GB+ minimum; the 5070 Ti covers all of that.
• Value buyers: at launch MSRP it delivers ~1.24× the tok/s per dollar of the 5080 (calculated 0.117 vs 0.094) for the same 16 GB model set.
• Buyers who want the Blackwell platform at 300 W rather than 360 W TDP, with the same GDDR7 / PCIe 5.0 x16 base as the 5080.
• Not a pick for 24B+ models: Mistral Small 3.2 24B (min 20 GB), Gemma 3 27B (min 24 GB) and Qwen 3 32B (min 24 GB) exceed the card; look at 24 GB+ cards instead.

15. Who should buy the RTX 5080

• Anyone who wants the fastest sourced decode in the 16 GB class — 94.14 vs 87.54 tok/s same-lab Qwen3-8B (~7.5% faster) and 20% more CUDA cores (10,752 vs 8,960).
• Image-generation users: the 5080 has sourced SDXL Turbo (65 img/min, ComfyUI) and UL Procyon image rows (SD 1.5 FP16 4,650 / INT8 55,683 / SDXL FP16 4,257); the 5070 Ti has no image rows at all.
• Buyers for whom $250 over the 5070 Ti at launch MSRP is acceptable for 7.1% more bandwidth (960 vs 896 GB/s) and a 60 W higher power class.
• Not a pick for 24B+ models — same 16 GB ceiling as the 5070 Ti; Gemma 3 27B and Qwen 3 32B at Q4 need a 24 GB card.

16. Evidence / source table

17. Data freshness date

24 September 2026. Product specs last verified in gpus.last_verified_at / products.price_checked_at on 2026-09-02. Same-lab bench date: 2025-12-09 (Qwen3-8B pair). One-sided 5080 rows: 2026-08-01 (localaimaster.com), 2025-01-29 (StorageReview), 2025-06-01 (techpowerup.com). No product_offers rows exist. This page does not invent newer street prices or unsourced tok/s.

Frequently asked questions

Is the RTX 5080 better than the RTX 5070 Ti for AI?

On the only same-lab sourced pair in our database — Qwen3-8B Q4_K_XL llama.cpp, 16K context, batch 1 (hardware-corner.net, 2025-12-09) — the RTX 5080 recorded 94.14 tok/s versus 87.54 tok/s on the RTX 5070 Ti, roughly 7.5% faster. Both cards carry the same 16 GB of GDDR7 on a 256-bit bus; the 5080 has higher bandwidth (960 vs 896 GB/s) and more CUDA cores (10,752 vs 8,960). It costs $250 more at launch MSRP ($999 vs $749). This page does not invent a single winner score; the verdict rests on that same-lab pair plus the spec delta, and the 5070 Ti is the better tok/s per launch dollar (calculated 0.117 vs 0.094).

Should I upgrade from RTX 5070 Ti to RTX 5080?

The $250 launch-MSRP premium buys roughly 7.5% faster sourced 8B decode (94.14 vs 87.54 tok/s same-lab), 20% more CUDA cores (10,752 vs 8,960), and 7.1% more memory bandwidth (960 vs 896 GB/s) — but no extra VRAM: both cards have 16 GB GDDR7, so the same models fit on both and the same models do not. Neither card runs Gemma 3 27B or Qwen 3 32B at Q4_K_M (both list a calculated minimum of 24 GB). Upgrade only if decode speed and image-generation throughput are worth $250 to you; for capacity-limited workflows the 5070 Ti fits exactly the same model set.

Which GPU is faster for local LLM inference?

On the same-lab llama.cpp Qwen3-8B Q4_K_XL pair (hardware-corner.net, 16K context, batch 1, 2025-12-09), the RTX 5080 recorded 94.14 tok/s versus 87.54 tok/s on the RTX 5070 Ti — about 7.5% faster. The 5080 also has one-sided rows: Llama-3.1-8B Q4_K_M via Ollama at 132 tok/s generation (localaimaster.com, 2026-08-01) and UL Procyon AI Text Generation scores of 4,400–4,790 across Phi-3.5-mini, Mistral-7B, Llama-3-8B and Llama-2-13B (StorageReview, 2025-01-29). The 5070 Ti has no sourced rows for those workloads — they are not a pair, just extra evidence on the 5080 side.

What do these cards cost?

Launch MSRP in the products table is $749 (RTX 5070 Ti) and $999 (RTX 5080), last price-checked 2026-09-02. Street price is NULL on both. No product_offers rows exist for either SKU. Used prices are not yet measured. Check live listings rather than treating MSRP as street.

Do the RTX 5070 Ti and RTX 5080 have the same VRAM?

Yes. Both record 16 GB of GDDR7 on a 256-bit bus (manufacturer spec / vendor spec page, confidence 0.95): the 5070 Ti at 896 GB/s and the 5080 at 960 GB/s. Both are Blackwell-generation discrete GPUs with PCIe 5.0 x16. The capacity is identical, so model-fit differences between the two cards come only from compute, not from memory size.

Can either card run Gemma 3 27B or Qwen 3 32B at Q4?

No. Gemma 3 27B lists a calculated minimum of 24 GB VRAM at Q4_K_M with a 4k context (about 20.16 GB calculated), and Qwen 3 32B lists 24 GB (about 22.72 GB calculated). Both cards have 16 GB, so neither meets the calculated minimum without aggressive quantization plus CPU offload. For 24 GB-class models, look at RTX 3090 / RTX 4090-class cards instead; this pair is not a 24B+ model fit.

Related: best GPU for local LLMs, best GPU for Stable Diffusion, RTX 5070 vs RTX 5070 Ti, RTX 5080 vs 4080 Super, RTX 5090 vs RTX 5080, RTX 5080 vs RTX 4090, RTX 5070 Ti product page, RTX 5080 product page, GPU index, Can it run?, DLSS 5 GPU support matrix.