RX 9070 GRE vs RTX 5070 for Local AI

1. 30-second verdict

This pair is a same-capacity cross-vendor clash at the same launch price — both cards carry 12 GB of discrete VRAM, so neither unlocks larger models than the other. The RTX 5070 (Blackwell, 2025) records GDDR7 at 672 GB/s, 6,144 CUDA cores, PCIe 5.0 x16, 250 W, launch MSRP $549. The RX 9070 GRE (AMD Radeon, 2026) records GDDR6 at 432 GB/s, PCIe 5.0 x16, 220 W, launch MSRP $549 (vendor spec pages). The 5070 holds a calculated 55.6% bandwidth advantage (672 / 432) and the only sourced local-LLM row in this pair: Qwen3-8B Q4_K_XL llama.cpp CUDA 12.8 at 59.13 tok/s (hardware-corner.net, 2025-12-09). The GRE has no benchmark row on any stack — there is no same-lab pair, so no speed winner is declared. Both list the same $549 launch MSRP; the GRE is 30 W lower TDP. Street price is not yet measured on either card. If your stack is CUDA, the 5070 is the only card with a sourced decode number; if you need ROCm or the lower power draw, the GRE delivers the same 12 GB at the same launch price.

2. Memory architecture

Both cards are discrete VRAM, not unified memory. Addressable GPU memory equals physical VRAM: 12 GB on both. The 5070 records a 192-bit bus with GDDR7 at 672 GB/s; the GRE records GDDR6 at 432 GB/s with bus width not recorded in the specifications table — a calculated 55.6% bandwidth advantage for the Blackwell card (672 / 432, 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; 14B-class models sit at or above the calculated 16 GB+ min and do not fit at Q4_K_M; 24B+ models do not fit on either. The difference between them is bandwidth, stack, 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.

This is the only benchmark row in this pair's database rows, and it is one-sided on the NVIDIA card. The RX 9070 GRE has no benchmark row on any stack (CUDA does not apply; ROCm/Vulkan rows are not yet measured). There is no same-lab pair, so this page does not ratio the 59.13 tok/s against the GRE or declare a speed winner. Image-generation speed on either card is not yet measured here.

5. Runtime compatibility

The two cards do not share a software stack. The 5070 is an NVIDIA CUDA discrete GPU (sourced stack in our rows: llama.cpp CUDA 12.8). The GRE is an AMD discrete GPU — CUDA does not apply; local-LLM stacks run via ROCm or Vulkan backends (llama.cpp ships Vulkan/ROCm builds). A CUDA row is not directly comparable to a Vulkan/ROCm row even when one is measured, so cross-stack parity is not assumed. Both cards record PCIe 5.0 x16. Fine-tune / multi-adapter stacks beyond the sourced rows are not yet measured on this pair.

6. Quantization compatibility

The only sourced generation row uses Q4_K_XL on Qwen3-8B (5070, CUDA). No quant row exists for the GRE on any stack. Q5/Q6/Q8 decode on this pair is not yet measured. Capacity is identical at 12 GB, so quant choice changes fit identically on both cards — the 5070's GDDR7 bandwidth advantage would show up at every quant level the DB rows cover, but no sourced row exists to quantify it on this pair.

7. Max practical context examples

The sourced llama.cpp row uses 16K context (Qwen3-8B, 5070). 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. Both cards have the same 12 GB, so context headroom is the same on both — bandwidth helps long-context decode speed, but no sourced long-context row exists on either card.

8. Power

Specifications record 220 W TDP (RX 9070 GRE) and 250 W TDP (RTX 5070) from the vendor spec pages. The 30 W difference is rated board power, not an inference-consumption measurement. Both are single-card, desktop-class power envelopes; plan PSU headroom above TDP and check partner board power input requirements. Energy cost per million tokens is not yet measured (no watt-hour rows on either card).

9. Current price

Price class is launch_msrp only. Products table: RX 9070 GRE $549 (no price_checked_at yet), RTX 5070 $549 (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 current listing (affiliate)
• RX 9070 GRE product page · RTX 5070 product page

10. Used price

Not yet measured. No used-condition offer rows exist for either SKU. The 5070 launched in 2025 and the GRE in 2026, so any used pool is still young — this page will not invent a median. A discounted used 5070 can be the right same-capacity buy if the bandwidth delta and CUDA stack matter to your workload; a discounted GRE is the right buy when ROCm or lower power matters more.

11. Performance per current dollar

Only one side is sourced, so a full price/perf ratio is not calculable on this pair. The 5070's sourced row gives 59.13 tok/s ÷ $549 = 0.108 tok/s per launch-MSRP dollar (calculated, labeled). The GRE has no sourced tok/s row, so no comparable figure exists — it is not zero, it is not-yet-measured. Both cards share the same $549 launch MSRP, so any future same-stack tok/s row on the GRE would be directly price-comparable at launch pricing. 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. 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. Mixing one AMD and one NVIDIA card in one runtime is generally not supported for a single model shard. Related: multi-GPU guide, workstations.

13. System requirements

Both: PCIe 5.0 x16 slot, single desktop-class board (vendor spec pages). GRE: 220 W TDP. 5070: 250 W TDP. Both are backwards-compatible with PCIe 4.0 motherboards and fit a standard desktop case and a PSU with headroom above TDP. CPU/RAM pairing for these two SKUs is not yet measured on this page.

14. Who should buy the RX 9070 GRE

• Anyone on an ROCm or Vulkan local-LLM stack — CUDA does not apply to this card, and the 5070's sourced CUDA row is not a cross-stack comparison.
• Buyers whose largest model fits 12 GB and who want the 30 W lower TDP (220 W vs 250 W rated board power) at the same $549 launch MSRP.
• Buyers who want a 2026-launch Radeon in the 9000-series stack at the same price as the 5070, accepting the recorded 432 GB/s GDDR6 bandwidth.
• Not a substitute for a 16 GB+ or 24 GB+ card when the model-fit min exceeds 12 GB (Phi-4/Qwen3 14B at 16 GB+ calculated min; Mistral Small 24B, Gemma 3 27B at 20–24 GB+).

15. Who should buy the RTX 5070

• Anyone on a CUDA stack (llama.cpp CUDA, ComfyUI) who wants the only sourced decode number in this pair — 59.13 tok/s on Qwen3-8B Q4_K_XL (hardware-corner.net, 2025-12-09).
• Buyers who want the calculated 55.6% memory-bandwidth advantage (GDDR7 672 GB/s vs GDDR6 432 GB/s), 6,144 CUDA cores, and Blackwell platform features (DLSS 4 Frame Generation listed on the NVIDIA 50-series product page — a display/gaming capability, not an AI-inference metric).
• Not a capacity pick over the GRE — both are 12 GB. If you need more VRAM, look at 16 GB+ or 24 GB+ cards instead of either.

16. Evidence / source table

17. Data freshness date

Frequently asked questions

Is the RX 9070 GRE better than the RTX 5070 for AI?

Both cards carry 12 GB of discrete VRAM, so neither unlocks larger models than the other — this is not a capacity comparison. The RTX 5070 records more memory bandwidth (GDDR7 at 672 GB/s vs GDDR6 at 432 GB/s, a calculated 55.6% advantage) and holds the only sourced local-LLM row in our database: Qwen3-8B Q4_K_XL llama.cpp CUDA 12.8, 16K context, batch 1 (hardware-corner.net, 2025-12-09) at 59.13 tok/s. No benchmark row exists for the RX 9070 GRE on any stack, so no same-lab pair exists and this page does not invent one. Both have the same $549 launch MSRP. The GRE counters with a lower TDP (220 W vs 250 W). If your stack is CUDA, the 5070 is the only card with a sourced decode number; if you need ROCm or lower power, the GRE gives the same 12 GB at the same launch price.

Which GPU is faster for local LLM inference?

Only one side is measured. The RTX 5070 has a sourced row: Qwen3-8B Q4_K_XL at 59.13 tok/s (llama.cpp CUDA 12.8, 16K context, batch 1, hardware-corner.net, 2025-12-09, tier 3, is_estimate=0). The RX 9070 GRE has no benchmark row in our database on any stack (CUDA does not apply; ROCm/Vulkan rows are not yet measured). There is no same-lab pair, so no winner can be declared on speed. The 5070 does hold a calculated 55.6% memory-bandwidth advantage (672 vs 432 GB/s from recorded specs), which typically helps decode speed, but no sourced row confirms the delta on this pair.

What do these cards cost?

Both record the same launch MSRP in the products table: $549. The RTX 5070 was last price-checked 2026-09-02; the RX 9070 GRE row has no price_checked_at yet. Street price is NULL on both and no product_offers rows exist. Check live listings rather than treating MSRP as street.

Which GPU uses more power?

The RTX 5070. Specifications record 250 W TDP for the RTX 5070 and 220 W TDP for the RX 9070 GRE (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.

Does the RX 9070 GRE support CUDA?

No. The RX 9070 GRE is an AMD Radeon GPU; CUDA is NVIDIA-only. Local-LLM stacks on the GRE run via ROCm, Vulkan, or OpenCL backends (llama.cpp supports Vulkan/ROCm builds). The only sourced benchmark row in this pair is a CUDA 12.8 llama.cpp run on the 5070, so it is not directly comparable to a Vulkan/ROCm run on the GRE even if one is later measured. This page does not assume cross-stack parity.

Should I upgrade from RX 9070 GRE to RTX 5070?

Both cards hold the same 12 GB of VRAM, so this is not a capacity upgrade. The 5070 offers GDDR7 at 672 GB/s (vs 432 GB/s), 6,144 CUDA cores, and the only sourced 8B decode row (59.13 tok/s), at the same $549 launch MSRP. The GRE has a 30 W lower TDP (220 W vs 250 W). If your models fit 12 GB and your stack is CUDA, the 5070 is the defensible pick on bandwidth and the sourced row; if you are on ROCm or value the lower power draw, the GRE matches capacity and launch price. If you need more than 12 GB, neither card helps — look at 16 GB+ or 24 GB+ cards instead.

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