RX 9070 vs RTX 5070 for Local AI

Frame: 16 GB Radeon vs 12 GB Blackwell, same $549 launch MSRP Specs: AMD / NVIDIA vendor spec pages Memory: discrete VRAM, not unified No same-lab bench pair — only one-sided sourced row

1. 30-second verdict

This pair splits on capacity versus bandwidth at the same launch price. The RX 9070 (AMD Radeon, RDNA 4, Navi 48, 2025) carries 16 GB of GDDR6 at 640 GB/s on a 256-bit bus, 3,584 stream processors, PCIe 5.0 x16, 220 W, launch MSRP $549. The RTX 5070 (Blackwell, GB205, 2025) carries 12 GB of GDDR7 at 672 GB/s on a 192-bit bus, 6,144 CUDA cores, PCIe 5.0 x16, 250 W, launch MSRP $549. The 5070 holds a calculated 5% bandwidth advantage (672 / 640) with 4 GB less VRAM, 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 9070 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 9070 is 30 W lower TDP and 4 GB larger. Street price is not yet measured on either card. If your models need 16 GB (Phi-4, Qwen3 14B at calculated 16 GB+ min), only the 9070 fits; if your models fit 12 GB and your stack is CUDA, the 5070 holds the sourced decode number and the bandwidth edge.

Buy the RX 9070 when your largest model needs 16 GB (Phi-4 / Qwen3 14B at Q4_K_M calculated 16 GB+ min), your stack is ROCm/Vulkan rather than CUDA, or you want the 30 W lower TDP and 4 GB more capacity at the same $549 launch MSRP. Buy the RTX 5070 when 12 GB covers your models and your local-LLM stack is CUDA (llama.cpp/ComfyUI) — you get the sourced 59.13 tok/s 8B decode, GDDR7 672 GB/s bandwidth, and 6,144 CUDA cores at the same launch price, accepting 250 W and the smaller memory pool.

2. Memory architecture

Both cards are discrete VRAM, not unified memory. Addressable GPU memory equals physical VRAM: 16 GB on the 9070, 12 GB on the 5070. The 9070 records a 256-bit bus with GDDR6 at 640 GB/s; the 5070 records a 192-bit bus with GDDR7 at 672 GB/s — a calculated 5% bandwidth advantage for the Blackwell card (672 / 640, from recorded specifications) despite the smaller pool. ECC is not recorded for either card. Neither card records NVLink support. Two of either card stay 2×16 GB or 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.

Model (DB)	Calculated min (DB)	RX 9070 16 GB	RTX 5070 12 GB
Llama 3.1 8B (8.03B)	8 GB VRAM at Q4_K_M, 4k (calculated)	Fits	Fits
Qwen 3 8B	8 GB VRAM at Q4_K_M, 4k	Fits	Fits
Phi-4 (14.66B)	16 GB+ at Q4_K_M (calculated)	Fits at calculated min	Does not meet calculated min
Qwen3 14B (14.77B)	16 GB+ at Q4_K_M (calculated)	Fits at calculated min	Does not meet calculated min
FLUX.1 dev (12B)	12 GB NF4/Q4 min; recommended 16 GB FP8	Meets recommended class	Meets calculated min
Mistral Small 3.2 24B	20 GB at Q4_K_M, 4k	Does not meet calculated min	Does not meet calculated min
Gemma 3 27B	24 GB at Q4_K_M, 4k	Does not meet calculated min	Does not meet calculated min

This is where the 4 GB gap bites: 8B-class models fit both with headroom; 14B-class models sit at the calculated 16 GB+ min and fit only on the 9070; 24B+ models fit on neither. FLUX.1 dev meets its calculated min on both and its recommended 16 GB FP8 class only on the 9070. The 5070's bandwidth edge does not change which models load — capacity does.

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.

Workload	Stack / notes	RX 9070	RTX 5070	Class
Qwen3-8B Q4_K_XL gen	llama.cpp llama-bench CUDA 12.8, 16K context, batch 1, Ubuntu 24.04; hardware-corner.net; 2025-12-09; tier 3	no row	59.13 tok/s	sourced, one-sided (CUDA)

This is the only benchmark row in this pair's database rows, and it is one-sided on the NVIDIA card. The RX 9070 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 9070 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 9070 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 9070 on any stack. Q5/Q6/Q8 decode on this pair is not yet measured. Capacity differs (16 vs 12 GB), so quant choice changes fit asymmetrically: a Q4_K_M 14B sits at the calculated 16 GB+ min and loads only on the 9070. 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. The 9070's extra 4 GB gives it more context headroom at a fixed quant; the 5070's 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) 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 $549 (price_checked_at 2026-09-02), 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:

10. Used price

Not yet measured. No used-condition offer rows exist for either SKU. Both launched in 2025, so any used pool is still young — this page will not invent a median. A discounted used 5070 can be the right buy if 12 GB covers your models and the CUDA stack matters to your workload; a discounted 9070 is the right buy when 16 GB capacity or ROCm 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 9070 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 9070 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 2×16 GB or 2×12 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. 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). 9070: 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

Anyone whose largest model sits at the calculated 16 GB+ min (Phi-4 14.66B, Qwen3 14B at Q4_K_M) — the 5070's 12 GB does not meet the calculated minimum.
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 who want the 30 W lower TDP (220 vs 250 W rated board power) and 4 GB more capacity at the same $549 launch MSRP.
FLUX.1 dev users who want the recommended 16 GB FP8 class rather than the 12 GB NF4/Q4 minimum.
Not a substitute for a 20 GB+ or 24 GB+ card when the model-fit min exceeds 16 GB (Mistral Small 24B, Gemma 3 27B).

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 5% memory-bandwidth advantage (GDDR7 672 GB/s vs GDDR6 640 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 9070 — 12 GB vs 16 GB. If your largest model needs 16 GB, the 5070 does not meet the calculated min.

16. Evidence / source table

Claim	Value	Source class	Origin
VRAM / type / bus / bandwidth / TDP / PCIe / SP / arch (9070)	16 GB GDDR6 256-bit 640 GB/s 220 W PCIe 5.0 x16 3,584 RDNA 4	vendor spec page (specifications + gpus tables)	https://www.amd.com/en/products/graphics/desktops/radeon/9000-series.html
VRAM / type / bus / bandwidth / TDP / CUDA / PCIe / arch (5070)	12 GB GDDR7 192-bit 672 GB/s 250 W 6144 PCIe 5.0 x16 Blackwell	manufacturer_spec (confidence 0.95)	specifications + gpus; NVIDIA 50-series product page on product source_url
Launch MSRP	$549 / $549	launch_msrp	products.msrp_usd; both price_checked_at 2026-09-02
Street / used	both unknown	unknown	street_price_usd NULL on both; no product_offers rows
Qwen3-8B llama.cpp row (5070 only)	59.13 tok/s	benchmark tier 3, not estimate, community_unverified (confidence 0.55)	https://www.hardware-corner.net/gpu-ranking-local-llm/ (2025-12-09)
9070 benchmark rows	none on any stack	unknown	no benchmarks rows for product 53; ROCm/Vulkan not yet measured
DLSS 4 Frame Generation	Blackwell feature (5070)	vendor product page (not a stored DB spec field)	NVIDIA 50-series product page (products.source_url)
Model-fit thresholds	8B min 8 GB; 14B-class min 16 GB; 24B+ min 20–24 GB	calculated model-fit	ai_models min_config / recommended_config

17. Data freshness date

Frequently asked questions

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

They split on capacity versus bandwidth. The RX 9070 carries 16 GB of GDDR6 at 640 GB/s; the RTX 5070 carries 12 GB of GDDR7 at 672 GB/s — a calculated 5% bandwidth advantage for the Blackwell card (672 / 640) despite 4 GB less VRAM. Both list the same $549 launch MSRP. The 5070 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 on any stack, so no same-lab pair exists and this page does not invent one. The 9070 counters with 4 GB more capacity (16 vs 12 GB) and a 30 W lower TDP (220 vs 250 W). If your models need 16 GB, only the 9070 fits; if your models fit 12 GB and your stack is CUDA, the 5070 holds the sourced decode number and the bandwidth edge.

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 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 5% memory-bandwidth advantage (672 vs 640 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. Both were last price-checked 2026-09-02. 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 (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 support CUDA?

No. The RX 9070 is an AMD Radeon GPU; CUDA is NVIDIA-only. Local-LLM stacks on the 9070 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 9070 even if one is later measured. This page does not assume cross-stack parity.

Should I upgrade from RX 9070 to RTX 5070?

That is a capacity downgrade, not a sideways move. The 9070 carries 16 GB; the 5070 carries 12 GB. If Phi-4 or Qwen3 14B at Q4_K_M (calculated 16 GB+ min) matters to you, the 5070 does not meet the calculated minimum while the 9070 does. The 5070 offers GDDR7 at 672 GB/s (vs 640 GB/s), 6,144 CUDA cores, and the only sourced 8B decode row (59.13 tok/s), at the same $549 launch MSRP. 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 need 16 GB or run ROCm, keep or buy the 9070.