RTX 5070 vs RTX 4070 for Local AI

1. 30-second verdict

This is one of the few pairs in our database with a real same-lab benchmark pair — both cards are NVIDIA, both ran on the same stack, so the numbers are directly comparable. Same capacity, same launch price: the RTX 5070 (Blackwell GB205, 2025) and the RTX 4070 (Ada Lovelace AD104, 2023) each record 12 GB of discrete VRAM and a $549 launch MSRP. On the sourced Qwen3-8B Q4_K_XL run (llama.cpp llama-bench CUDA 12.8, 16K context, batch 1, Ubuntu 24.04, hardware-corner.net, 2025-12-09), generation favors the 5070: 59.13 tok/s vs 52.07 tok/s, a calculated 13.6% advantage. Prompt processing inverts: 2064.25 tok/s on the 4070 against 1600.78 tok/s on the 5070, a calculated 29.0% advantage for the older card. Specs follow the same split: GDDR7 at 672 GB/s against GDDR6X at 504 GB/s (calculated +33.3% for the 5070), 6,144 against 5,888 CUDA cores, PCIe 5.0 against 4.0 x16 — but the 4070 draws 200 W against 250 W. Neither card is faster across the board; the winner depends on whether your workload is generating or ingesting.

2. Memory architecture

Both cards are discrete VRAM — the gpus table records memory_architecture=discrete_vram with gpu_addressable_gb=12 and physical_memory_gb=12 on both. Capacity is identical, so this is not a capacity comparison: neither card runs a model the other cannot. What separates them is memory speed. The RTX 5070 records GDDR7 on a 192-bit bus at 672 GB/s; the RTX 4070 records GDDR6X on a 192-bit bus at 504 GB/s — a calculated 33.3% bandwidth advantage for the Blackwell card (672 / 504, from recorded specifications). Note that GDDR6X and GDDR7 behave differently under sustained load and in power draw, and the DB does not record those curves, so this page does not extrapolate beyond the recorded peak figures. ECC is not recorded for either card. Neither records NVLink support. Two of either card stay 2×12 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.

Every row lands identically on both cards — that is the expected result of equal capacity, and it is the honest reason this page's speed section matters more than its fit section. 8B-class models fit both with headroom. 14B-class models sit at or above the calculated 16 GB+ minimum and fit on neither card. FLUX.1 dev meets only the 12 GB minimum on both, and neither reaches the 16 GB FP8 recommended configuration. 24B and 27B models need 20–24 GB and need a different card entirely. The 4070's own recommended-config text in our model records names the RTX 4070 as an example of a 12 GB tier card, which is consistent with the recorded specs.

4. Direct benchmark table

Only non-quarantined benchmarks rows. Both cards are NVIDIA, so the two rows are a same-stack, same-run pair — the exception on this site, not the rule. A blank cell means no sourced row — not zero. Estimate means is_estimate=1.

These four numbers are the only benchmark rows for either card, and they come from one run on one source, so they are a single-workload, single-source evidence set — not a general speed verdict. The ratios are calculated, not measured: 59.13 / 52.07 = 1.136, so generation is +13.6% for the 5070; 1600.78 / 2064.25 = 0.776, so prompt processing is +29.0% for the 4070. Two honest caveats: the 4070's prompt-processing lead runs against its own lower generation number on the same run, which is the opposite of what pure bandwidth would predict, so the recorded 504 GB/s figure does not by itself explain prompt throughput on this data; and both cards are measured at one workload (Qwen3-8B), one quant (Q4_K_XL), and one context length (16K). Image-generation and fine-tuning speed on either card is not yet measured here.

5. Runtime compatibility

Unlike the cross-vendor pairs on this site, both cards are NVIDIA and both run the same CUDA stack — that is what makes the rows above comparable at all. The sourced stack in our database is llama.cpp llama-bench CUDA 12.8, and any ComfyUI or PyTorch CUDA build applies to both. The cards differ at the interface level: the 5070 records PCIe 5.0 x16 and the 4070 PCIe 4.0 x16. That matters for host-to-device transfer on long prompts and for systems with fast SSDs or large model files, though no transfer-throughput rows exist in our database to quantify it. No software or driver incompatibility is recorded for either card in the pairs this page compares. Multi-adapter and fine-tuning stacks beyond the sourced rows are not yet measured.

6. Quantization compatibility

The only sourced rows use Q4_K_XL on Qwen3-8B, on both cards. Q5/Q6/Q8 decode on this pair is not yet measured. Because capacity is identical at 12 GB, quant choice changes fit identically on both cards — the 14B-class minimums and the FLUX.1 16 GB recommendation miss on both regardless of quant. Where quant and the cards do interact is throughput: the 5070's GDDR7 at 672 GB/s is the faster memory and would generally benefit higher quants, but our sourced rows cover only Q4_K_XL, so this page does not publish a per-quant throughput table it cannot source.

7. Max practical context examples

Both sourced runs use 16K context (Qwen3-8B Q4_K_XL). In our model records, Llama 3.1 8B has a native context of 131,072 tokens, Deepseek R1 Distill Llama 8B the same, and Qwen3 14B 40,960 tokens. Whether either 12 GB card holds the full native window at a given quant is not yet measured here. This is also where the prompt-processing split is most relevant in practice: the 4070 recorded the higher prompt-processing rate on the same run, and prompt throughput is the term that moves when you raise context length, so if you run long-context or RAG-indexing workloads the 4070's measured +29.0% prompt lead is the number to weigh. That is an inference from a single 16K-context measurement, not a measured long-context result.

8. Power

Specifications record 200 W TDP (RTX 4070) and 250 W TDP (RTX 5070) from the vendor spec pages, both at confidence 0.95. The 50 W difference favors the older card and is the clearest non-benchmark difference in this comparison. TBP (total board power) is not recorded for either row, so partner-board variance is not captured here. Rated board power is not an inference-consumption measurement: energy cost per million tokens is not yet measured on either card because no watt-hour rows exist.

9. Current price

Price class is launch_msrp only. Products table: RTX 5070 $549 (status available), RTX 4070 $549 (status eol); both price_checked_at 2026-09-02. street_price_usd is NULL on both. No product_offers rows exist for either SKU (0 rows each). The equal MSRP is the unusual part here — a 2023 part and a 2025 part launched at the same $549 — and in practice the 4070's used price is the only figure likely to differ from that number. Check live listings rather than treating MSRP as street:

• RTX 5070 current listing (affiliate)
• RTX 4070 current listing (affiliate)
• RTX 5070 product page · RTX 4070 product page

10. Used price

Not yet measured. No used-condition offer rows exist for either SKU, and this page will not invent a median. What the database does record is status: the RTX 4070 is eol and the RTX 5070 available. For a card whose recorded launch MSRP ($549) matches its successor's, a used 4070 is worth pricing carefully — it is the only scenario in this pair where the two cards do not cost the same, and it is the scenario where the 4070's prompt-processing lead and 200 W TDP matter most. The sourced rows are all from a single December 2025 run; whether an aged 4070 still performs like that is not yet measured.

11. Performance per current dollar

This is the one page in the lane where per-dollar figures are calculable on both sides at once, because both cards have a sourced same-run row and identical launch MSRP. All four figures below are calculated from sourced rows and launch MSRP, not measured at street:

Because the two cards share a $549 denominator, these figures differ only by the numerator — the per-dollar story is the same as the raw throughput story, with the 5070 ahead on generation and the 4070 ahead on prompt processing. No street or offer rows exist to recompute either figure at current prices.

12. Multi-GPU considerations

Neither card records NVLink support in the database (null on both). Desktop GeForce cards in this class have no NVLink bridge — two of either card are 2×12 GB, not 24 GB of one pool, unless the runtime shards (tensor parallel / pipeline / expert offload). Dual-card tok/s for this pair is not yet measured. Because both cards are CUDA, a two-card 5070+4070 build is at least stack-compatible, but a mixed Ada/Blackwell pair for one sharded model is not yet measured here. Related: multi-GPU guide, workstations.

13. System requirements

RTX 5070: PCIe 5.0 x16 slot, 250 W TDP. RTX 4070: PCIe 4.0 x16 slot, 200 W TDP (vendor spec pages, confidence 0.95). The 4070 runs fine in a PCIe 4.0 x16 slot; the 5070 requires a PCIe 5.0 x16 board to reach its full host-interface link, though it is electrically backwards-compatible with older slots. Both fit a standard desktop case with a PSU that has headroom above TDP — the 50 W difference is what separates their PSU requirements. CPU/RAM pairing for these two SKUs is not yet measured on this page.

14. Who should buy the RTX 5070

• Generation-heavy workloads where 12 GB is enough and you want the sourced 59.13 tok/s Qwen3-8B Q4_K_XL decode — a calculated 13.6% over the 4070 on the same run.
• Buyers who want GDDR7 at 672 GB/s (calculated +33.3% over the 4070's 504 GB/s) and 6,144 CUDA cores over 5,888.
• Buyers on a PCIe 5.0 platform, or who want the newest architecture in our database with a recorded available status and a full driver runway.
• Buyers who value the 4070 SUPER comparison: at $56.21 tok/s on the same sourced Qwen3-8B Q4_K_XL run, the SUPER's own row sits between the 4070's 52.07 and the 5070's 59.13.
• Not a capacity pick over the 4070 — both are 12 GB. If you need Phi-4 or Qwen3 14B at Q4_K_M, look at 16 GB+ parts instead.

15. Who should buy the RTX 4070

• Prompt-ingestion-heavy workloads: the card leads prompt processing on the same sourced run (2064.25 vs 1600.78 tok/s, calculated +29.0%), which is the term that scales with long context and RAG indexing.
• Buyers who want 200 W instead of 250 W TDP — a 50 W reduction that matters in a small case or a shared power budget.
• Used buyers who find a 4070 below the $549 launch MSRP of its own successor, since the recorded MSRP of these two cards is identical and the 4070's status in our database is eol.
• Not a pick if your workload is pure generation: it is the slower of the two on the sourced generation row (52.07 vs 59.13 tok/s).

16. Evidence / source table

17. Data freshness date

Frequently asked questions

Is the RTX 5070 faster than the RTX 4070 for AI?

On token generation, yes, and we have the same-lab rows to show it: Qwen3-8B Q4_K_XL llama.cpp CUDA 12.8, 16K context, batch 1, Ubuntu 24.04 (hardware-corner.net, 2025-12-09) records 59.13 tok/s on the RTX 5070 against 52.07 tok/s on the RTX 4070, a calculated 13.6% generation advantage for the newer card. On prompt processing the ranking inverts on the same run: 2064.25 tok/s on the 4070 against 1600.78 tok/s on the 5070, a calculated 29.0% advantage for the older card. So the honest answer is workload-dependent — the 5070 wins generation, the 4070 wins prompt ingestion, and neither is the faster card across the board.

Do both cards have the same VRAM?

Yes. Both record 12 GB of discrete VRAM: the RTX 5070 as 12 GB GDDR7 on a 192-bit bus, the RTX 4070 as 12 GB GDDR6X on a 192-bit bus. This is not a capacity comparison — neither card unlocks a larger model than the other. What differs is memory speed (672 GB/s vs 504 GB/s, a calculated 33.3% advantage for the 5070) and bus behavior under load, not how much memory is available.

What do these cards cost?

Both record the same $549 launch MSRP in the products table, and both rows were last price-checked 2026-09-02. That is the important part of the comparison: the 4070 is recorded as eol in our database while the 5070 is recorded as available, so the used market is the only place the 4070 is likely to differ in price. Street price is NULL on both and no product_offers rows exist for either SKU, so this page will not invent a used median.

Which card uses less power?

The RTX 4070. Specifications record 200 W TDP for the RTX 4070 and 250 W TDP for the RTX 5070 (vendor spec pages). The 50 W difference is rated board power, not a measurement of inference consumption. Energy cost per million tokens is not yet measured on either card.

Is the RTX 4070 still worth buying?

On recorded specs, it wins two places: 200 W versus 250 W TDP, and prompt-processing throughput (2064.25 vs 1600.78 tok/s on the same sourced Qwen3-8B Q4_K_XL run). It also costs the same $549 launch MSRP as the 5070, which is unusual for a two-generation-old part. It loses on generation speed (52.07 vs 59.13 tok/s), memory bandwidth (504 vs 672 GB/s), CUDA cores (5,888 vs 6,144), and PCIe generation. Our database records its status as eol, so supply and driver-support runway both favor the newer card for a fresh purchase.

Should I keep my RTX 4070 or upgrade to the RTX 5070?

If your models fit 12 GB, the upgrade is a speed and efficiency move, not a capacity move. The sourced same-lab rows put generation at 59.13 vs 52.07 tok/s (5070 +13.6%, calculated) and prompt processing at 1600.78 vs 2064.25 tok/s (4070 +29.0%, calculated) — so if your workload is generation-heavy the 5070 is the upgrade, and if it is prompt-ingestion-heavy (long documents, large context, RAG indexing) the 4070 is not obviously beaten at this tier. You would also move from PCIe 4.0 to 5.0 x16 and from 200 W to 250 W TDP. If you need more than 12 GB, neither card solves that.

What about the RTX 4070 SUPER instead?

The RTX 4070 SUPER is a separate product in our database (id 24) at a higher $599 launch MSRP, and it is the only card in the 4070 family we have a second sourced source for: alongside hardware-corner.net it also carries a myaihardware.com Llama-3.1-8B Q4_K_M row (92 tok/s generation, 2900 tok/s prompt, tested 2026-05-22) and its own hardware-corner Qwen3-8B Q4_K_XL row at 56.21 tok/s generation. If you are cross-shopping this tier, the SUPER is the denser 4070-family option.

Related: best GPU for local LLMs, best GPU for Stable Diffusion, RTX 5070 vs RTX 5070 Ti, RX 9070 vs RTX 5070, RTX 4070 Ti SUPER vs RTX 4080 SUPER, RTX 5070 Ti vs RTX 4080 SUPER, best GPU under $500, RTX 5070 product page, RTX 4070 product page, GPU index, Can it run?.