RTX 5070 vs RTX 4070 SUPER for Local AI

1. 30-second verdict

This is the rare comparison pair that has a genuine same-lab benchmark: our database holds the same model, quant, stack, and context measured on both cards. Both carry 12 GB of discrete VRAM, so neither unlocks larger models. The RTX 5070 (Blackwell, GB205, 2025) records GDDR7 at 672 GB/s, 192-bit, PCIe 5.0 x16, 250 W, launch MSRP $549. The RTX 4070 SUPER (Ada Lovelace, AD104, 2024, status eol in the database) records GDDR6X at 504 GB/s, 192-bit, PCIe 4.0 x16, 220 W, launch MSRP $599 (vendor spec pages). On the same-lab row — Qwen3-8B Q4_K_XL, llama.cpp CUDA 12.8, 16K context, batch 1, hardware-corner.net 2025-12-09 — the 5070 generates faster at 59.13 vs 56.21 tok/s (calculated +5.2%) while the 4070 SUPER processes prompts faster at 2525.76 vs 1600.78 tok/s (calculated +57.8%). The 5070 also holds a calculated 33.3% bandwidth advantage (672 / 504) and launched $50 cheaper. Street price is not yet measured on both.

2. Memory architecture

Both cards are discrete VRAM, not unified memory. Addressable GPU memory equals physical VRAM: 12 GB on both (gpus.gpu_addressable_gb 12, physical_memory_gb 12, memory_architecture discrete_vram on each row). The bus width is identical at 192-bit on both — this is a pure memory-generation comparison. The 5070 records GDDR7 at 672 GB/s; the 4070 SUPER records GDDR6X at 504 GB/s, a calculated 33.3% advantage for the Blackwell card (672 / 504, from recorded specifications). ECC is not recorded for either card. Neither card 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.

Both cards land on the same side of every fit line — same capacity means same fit. 8B-class models fit with headroom; FLUX.1 dev meets the calculated 12 GB NF4/Q4 min on both (neither meets the 16 GB FP8 recommendation); 14B-class and larger LLMs sit above the calculated 16 GB+ min and do not fit; 24 GB+ video models do not fit on either. The difference between them is bandwidth, measured speed split, and power, not which models load.

4. Direct benchmark table

Only non-quarantined benchmarks rows. 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 Q4_K_XL rows are a real pair — same model, same quant, same stack, same 16K context, same lab, same date — so those two lines can be compared directly: the RTX 5070 leads generation by a calculated +5.2% (59.13 / 56.21) and the RTX 4070 SUPER leads prompt processing by a calculated +57.8% (2525.76 / 1600.78). The Llama-3.1-8B rows are one-sided on the 4070 SUPER (different model, different quant, different stack build, 4k context, different date), so this page does not ratio the 92 tok/s against anything on the 5070 and does not treat it as part of a pair. All four rows are is_estimate=0 at source tier 3 (community_unverified, confidence 0.55) — real measurements, community-sourced. Image-generation speed on either card is not yet measured here.

5. Runtime compatibility

Both cards are NVIDIA CUDA discrete GPUs, so the stack is shared and the same-lab rows above are directly comparable: llama.cpp CUDA 12.8 and llama.cpp b3500 CUDA both run on either card. There is no cross-stack caveat for this pair. Both record a 192-bit bus; PCIe generation differs (5.0 x16 on the 5070, 4.0 x16 on the 4070 SUPER), which affects large model load times rather than steady-state decode. Fine-tune and multi-adapter stacks beyond the sourced rows are not yet measured on this pair. Note that the 4070 SUPER's database status is eol, which matters for future driver and runtime support planning more than for today's runs.

6. Quantization compatibility

Sourced rows cover two quants: Q4_K_XL on Qwen3-8B (paired on both cards, CUDA 12.8) and Q4_K_M on Llama-3.1-8B (4070 SUPER only, b3500 CUDA). No Q5, Q6, or Q8 row exists on either card, so higher-quant decode on this pair is not yet measured. Capacity is identical at 12 GB, so quant choice changes fit identically on both cards. Because both cards are bandwidth-bound at decode, the 5070's recorded 672 GB/s should widen its generation lead as quant rises — but no sourced row confirms that, and this page does not extrapolate a number from a trend.

7. Max practical context examples

The paired Qwen3-8B Q4_K_XL rows use 16K context on both cards. Qwen 3 8B native context in our model record is 40,960 tokens; Llama 3.1 8B native context is 131,072 tokens, and its calculated requirement grows to about 9.6 GB at Q4_K_M with a 32k context. The one-sided 4070 SUPER Llama row uses a 4k context with a 512-token prompt. Whether either GPU holds the full native window at a given quant is not yet measured here. Both cards have the same 12 GB, so context headroom is identical on both — bandwidth helps long-context decode, but the sourced long-context evidence is the single 16K pair.

8. Power

Specifications record 250 W TDP (RTX 5070) and 220 W TDP (RTX 4070 SUPER) from the NVIDIA vendor spec pages. The 30 W difference is rated board power, not an inference-consumption measurement. Both are single-card, desktop-class power envelopes; the 5070 needs a PSU with headroom above 250 W. Energy cost per million tokens is not yet measured (no watt-hour rows on either card), so this page does not convert TDP into a cost-per-million-tokens delta — that would require measured wall power under load, which we do not hold.

9. Current price

Price class is launch_msrp only. Products table: RTX 5070 $549, RTX 4070 SUPER $599 (both price_checked_at 2026-09-02). The newer Blackwell card therefore launched $50 cheaper than the Ada refresh it replaces at this tier. 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 current listing (affiliate)
• RTX 4070 SUPER current listing (affiliate)
• RTX 5070 product page · RTX 4070 SUPER product page

10. Used price

Not yet measured. No used-condition offer rows exist for either SKU. The 4070 SUPER launched in 2024 and its database status is eol, so a used pool exists and should widen, but this page will not invent a median or a depreciation curve. Practical note: the 4070 SUPER's prompt-processing lead on the paired row (+57.8%) means used value depends on your workload split — a prompt-heavy buyer can rationally prefer a cheap used 4070 SUPER over a pricier 5070, while a generation-heavy buyer should weight the 5070's +5.2% generation lead and its lower launch MSRP.

11. Performance per current dollar

Because the Qwen3-8B Q4_K_XL rows are same-lab, a launch-MSRP price-per-speed figure is calculable and labeled as such:

• RTX 5070 generation: 59.13 ÷ $549 = 0.1077 tok/s per launch-MSRP dollar (calculated).
• RTX 4070 SUPER generation: 56.21 ÷ $599 = 0.0938 tok/s per launch-MSRP dollar (calculated).
• RTX 5070 prompt: 1600.78 ÷ $549 = 2.916 tok/s per launch-MSRP dollar (calculated).
• RTX 4070 SUPER prompt: 2525.76 ÷ $599 = 4.216 tok/s per launch-MSRP dollar (calculated).

On the generation row the 5070 is the better launch-MSRP buy (calculated +14.8% per dollar); on the prompt row the 4070 SUPER is (calculated +44.6% per dollar). These are launch-MSRP figures only: no street or offer rows exist for either SKU, so none of these ratios can be recomputed at current prices. The one-sided Llama-3.1-8B 4070 SUPER row is deliberately excluded from the dollar ratios, since no 5070 counterpart exists to pair it with.

12. Multi-GPU considerations

Neither card records NVLink support in the database, and desktop 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. One genuinely useful two-card configuration for this tier is a 5070 plus a 4070 SUPER to cover both workload halves, but any claim about that combination's throughput is not yet measured and this page does not estimate it. Related: multi-GPU guide, workstations.

13. System requirements

Both: 192-bit bus, single desktop-class board (NVIDIA vendor spec pages). The 5070 records PCIe 5.0 x16 and 250 W; the 4070 SUPER records PCIe 4.0 x16 and 220 W. Both are backwards-compatible with PCIe 3.0 and 4.0 motherboards and fit a standard desktop case with a PSU headroom above TDP. The 5070's PCIe 5.0 link only pays off on a PCIe 5.0 board; on PCIe 4.0 it trains down to the older generation's bandwidth. Both need a modern NVIDIA driver stack for the CUDA rows cited above. CPU/RAM pairing for these two SKUs is not yet measured on this page.

14. Who should buy the RTX 5070

• Generation-bound local inference: the paired row shows the faster decoder at 59.13 tok/s vs 56.21 tok/s (calculated +5.2%) on Qwen3-8B Q4_K_XL, the standard interactive case.
• Buyers who want the calculated 33.3% bandwidth advantage (GDDR7 672 GB/s vs GDDR6X 504 GB/s), Blackwell, PCIe 5.0 x16, and the better launch-MSRP per generation dollar — at 250 W.
• Buyers planning a multi-year horizon: the 4070 SUPER's database status is eol, while the 5070 is available.
• Buyers who do prompt-heavy work and should instead check the 4070 SUPER below, or who need more than 12 GB and should look at 16 GB or 24 GB cards.

15. Who should buy the RTX 4070 SUPER

• Prompt-heavy workloads: on the paired row the 4070 SUPER leads prompt processing at 2525.76 vs 1600.78 tok/s (calculated +57.8%) — the larger share of wall-clock time in document ingestion, RAG indexing, and long-context prefill.
• Buyers who want the 30 W lower TDP (220 W vs 250 W rated board power) and the only sourced Llama-3.1-8B row in the pair (92 tok/s generation, 2900 tok/s prompt, myaihardware.com 2026-05-22 — one-sided, so not comparable to a 5070 figure).
• Buyers who find a used 4070 SUPER materially cheaper than a 5070 and whose workload leans toward prompt processing, where the paired row favors the older card.
• Not the pick for a fresh midrange build at list price: the 5070 launched $50 cheaper, is available where the 4070 SUPER is eol, and leads generation on the paired row.

16. Evidence / source table

17. Data freshness date

Frequently asked questions

Is the RTX 5070 better than the RTX 4070 SUPER for AI?

Both cards carry 12 GB of discrete VRAM, so neither unlocks larger models than the other. The RTX 5070 records a calculated 33.3% memory-bandwidth advantage (GDDR7 at 672 GB/s against the 4070 SUPER GDDR6X at 504 GB/s), 250 W against 220 W TDP, and a $50 lower launch MSRP ($549 vs $599). On our one same-lab row (Qwen3-8B Q4_K_XL, llama.cpp CUDA 12.8, 16K context, batch 1, hardware-corner.net 2025-12-09) the 5070 generates faster at 59.13 vs 56.21 tok/s (calculated +5.2%), while the 4070 SUPER processes prompt faster at 2525.76 vs 1600.78 tok/s (calculated +57.8%).

Which GPU is faster for local LLM inference?

It depends on which half of the work you care about, on the one same-lab pair we hold. For token generation on Qwen3-8B Q4_K_XL the RTX 5070 leads at 59.13 tok/s against 56.21 tok/s (calculated +5.2%). For prompt processing on the same run the RTX 4070 SUPER leads at 2525.76 tok/s against 1600.78 tok/s (calculated +57.8%). Both rows are the same model, quant, stack, and context, so the comparison is valid. The 4070 SUPER also holds a one-sided Llama-3.1-8B Q4_K_M row (92 tok/s generation, 2900 tok/s prompt) that has no 5070 counterpart, so it cannot be paired.

What do these cards cost?

The RTX 5070 records a $549 launch MSRP and the RTX 4070 SUPER a $599 launch MSRP, so the newer card launched $50 cheaper in the products table. Both were last price-checked 2026-09-02. Street price is NULL on both and no product_offers rows exist for either SKU. Check live listings rather than treating MSRP as street, and note the 4070 SUPER row carries status eol in the database.

Which GPU uses more power?

The RTX 5070. Specifications record 250 W TDP for the RTX 5070 and 220 W TDP for the RTX 4070 SUPER from the NVIDIA vendor spec pages. The 30 W difference is rated board power, not a measurement of inference consumption. Energy cost per million tokens is not yet measured on either card.

Do both cards support CUDA?

Yes. Both are NVIDIA GeForce discrete GPUs running the same CUDA stack (our rows use llama.cpp CUDA 12.8 and llama.cpp b3500 CUDA), so a same-lab comparison is valid here and no cross-stack caveat applies. That is what makes the Qwen3-8B Q4_K_XL row a genuine pair rather than two unrelated numbers.

Should I buy the RTX 5070 or keep the RTX 4070 SUPER?

Both hold the same 12 GB, so this is not a capacity question. The 5070 gives the calculated 33.3% bandwidth advantage, the faster generation on our same-lab row (+5.2%), the faster memory generation, and a $50 lower launch MSRP, for 30 W more and no prompt-processing lead. The 4070 SUPER is the pick for prompt-heavy or retrieval workloads on our row, for the 30 W lower draw, or when a discounted used unit costs materially less — the database records its status as eol. If you need more than 12 GB, neither card helps.

Related: best GPU for local LLMs, best GPU under $500, RTX 5070 vs RTX 5070 Ti, RTX 5070 Ti vs RTX 5080, RTX 5080 vs RTX 4080 SUPER, RTX 5070 product page, RTX 4070 SUPER product page, GPU index, Can it run?.