TL;DR: One RTX 5090 ($4,329 on Amazon, August 2026) is the fastest way to run anything that fits in 32GB — 205 tok/s on Qwen3.5-35B MoE. Two Radeon AI PRO R9700s ($3,050–$3,200 for the pair) are slower per token but hold 64GB, which runs a 70B Q4 fully on GPU — something the 5090 cannot do at any price.

	RTX 5090 (1× card)	Dual R9700 (2× cards)	Single R9700
Best for	Fastest speed on models ≤32GB, CUDA tooling	Full-GPU 70B Q4, long-context prefill on a budget	Best value per GB under $1,700
VRAM / bandwidth	32GB / 1,792 GB/s	64GB / 640 GB/s per card	32GB / 640 GB/s
Price (Aug 2026)	~$4,329 Amazon; AIB $2,900–$5,000+	~$3,050–$3,200 street ($2,598 at MSRP)	$1,522–$2,139 in-stock listings
35B MoE decode	205 tok/s	~115–164 tok/s	127–163 tok/s
70B Q4 decode	Doesn’t fit (42.5GB > 32GB)	11.49 tok/s, fully on GPU	Doesn’t fit
The catch	Costs more than both R9700s combined, still capped at 32GB	ROCm setup, 600W of GPU, decode doesn’t scale with card count	Same 32GB ceiling as the 5090, 1/3 the bandwidth

Honest take: Buy the RTX 5090 if the models you actually run fit in 32GB and you’ll use the speed daily; buy dual R9700s only if running 48–64GB models locally is the whole point — for everyone else, one R9700 now plus a second later is the smarter path to the same 64GB.

The question sounds like a straight spec-sheet fight: $4,329 for 32GB of the fastest VRAM money can buy, or roughly $3,150 for 64GB of slower VRAM split across two cards. But an r/LocalLLaMA benchmark shootout from August 2026 — the first to run the RTX 5090, single and dual R9700s, and AMD’s AI 395 through identical llama-bench settings — shows the real answer is a fork in the road, not a winner. Which side of the fork you’re on depends entirely on one number: the file size of the biggest model you actually want to run.

The August 2026 price math

Start with what these actually cost, because MSRPs stopped meaning anything months ago.

The RTX 5090’s $1,999 MSRP is a fiction we’ve been documenting since the GDDR7 price-hike wave in May. As of August 2026, Amazon street pricing sits around $4,329, with AIB cards ranging from $2,900 for the occasional budget model to over $5,000 for liquid-cooled flagships. If you can find one near $3,000, that changes the math below meaningfully — but most buyers won’t.

The R9700 launched at $1,299 MSRP with partner cards listed from $1,244, and unlike NVIDIA’s flagship, it has intermittently sold at that price. August 2026 in-stock listings run $1,522–$2,139 on the resale market, but patient buyers catch MSRP restocks — which puts a pair anywhere from $2,598 (both at MSRP) to about $3,200 (both at typical street). We’ll use ~$3,050–$3,200 as the realistic pair price.

So the real gap between one 5090 and two R9700s is roughly $1,100–$1,300 — in AMD’s favor, for twice the VRAM. That’s the trade on the table.

What the shootout actually measured

The benchmark that prompted this comparison ran llama-bench build 8463 with identical flags across every machine — -ngl 99 -fa 1 -p 2048 -n 256 -b 512 — on four models chosen to bracket the VRAM tiers: Qwen2.5 32B (dense), Qwen3.5 35B MoE, Qwen2.5 70B (dense), and Qwen3.5 122B MoE. Same build, same flags, same models is rarer than it should be in GPU comparisons, which is what makes this dataset useful.

If you want to reproduce the test on your own hardware, this is the whole command:

./llama-bench -m qwen3.5-35b-a3b-q4_k_xl.gguf \
  -ngl 99 -fa 1 -p 2048 -n 256 -b 512
# Expected output on an RTX 5090 (build 8463):
# | model             | backend | pp2048 t/s | tg256 t/s |
# | qwen3.5-35b-a3b   | CUDA    |  5988.83   |  205.36   |

Three results from that thread decide this entire comparison:

Result 1: the RTX 5090 dominates everything that fits. On Qwen3.5-35B MoE it posted 5,988.83 tok/s prompt processing and 205.36 tok/s generation. An independent test in llama.cpp discussion #19890 — different build, different flags — landed in the same place: 7,026 tok/s prefill and 194 tok/s decode on the same model class, versus 2,713 and 127.4 for a single R9700 on Vulkan. Two independent datasets, one conclusion: for models under 32GB, the 5090 is roughly 2.6–3.4× faster at prompt processing and about 1.5× faster at generation.

Result 2: dual R9700s run the 70B the 5090 can’t load. Qwen2.5 70B at Q4 needs more than 32GB — Llama 3.3 70B Q4_K_M is 42.5GB as a reference point for the class — so the 5090 simply cannot hold it without spilling layers to system RAM, where performance falls off a cliff. The dual-R9700 box ran it fully on GPU at 11.49 tok/s generation with roughly 600 tok/s prompt processing under ROCm. Not fast. Fully resident, warrantied, and local.

Result 3: the backend matters more on AMD. ROCm consistently won prompt processing across the AMD configurations. Vulkan sometimes edged ahead on generation — but the thread documents vk::DeviceLostError crashes under sustained multi-GPU load. That’s a real error with a real fix: for dual-card R9700 setups, run the ROCm backend, not Vulkan, and accept a few tok/s less on decode in exchange for it finishing the job. (Single-card Ollama users have the opposite problem — Ollama’s bundled ROCm libraries lack gfx1201 kernels, and the fix is enabling Vulkan; we covered that failure mode in the R9700 hardware guide.)

Where the RTX 5090 wins

Everything at or under 32GB, and it isn’t close.

The bandwidth argument is architectural. The 5090’s GDDR7 moves 1,792 GB/s; each R9700’s GDDR6 moves 640 GB/s. Single-stream token generation is bandwidth-bound, and — this is the part multi-GPU newcomers consistently get wrong — adding a second card does not add bandwidth to a single generation stream. Community dual-R9700 testing on ROCm 7.2.2 makes this brutally concrete: on dense Qwen3.6 27B Q5_K_M, going from one card to two moved prompt processing from 611 to 1,216 tok/s (nearly perfect 2× scaling) while token generation went from 24.85 to 24.31 tok/s — slightly slower. On Gemma 4 26B-A4B, dual cards took prefill from 1,782 to 3,513 tok/s while decode dropped from 74.26 to 66.56. Layer splitting doubles capacity and parallelizes prefill; it does nothing for the speed at which one answer streams out. The same physics applied to dual RTX 3090 builds, and it isn’t changing.

The ecosystem argument is quieter but just as real. CUDA works with everything on day one: every quantization format, every inference engine, ComfyUI custom nodes, fine-tuning stacks, the lot. ROCm 7.2 has made RDNA4 a first-class citizen in llama.cpp and LM Studio, and the tuning ceiling keeps rising — llama.cpp discussion #21043 pushed dual-R9700 MoE decode from 115 to 163.7 tok/s over months of driver and batching work. But that history is the point: on AMD, peak performance is something the community excavates over time. On NVIDIA it’s the default.

And there’s simplicity. One card, one PCIe slot, 575W board power, no split-mode flags, no thinking about lane topology. Dual R9700s want 600W of GPU headroom, a motherboard that feeds both slots properly, and a case that clears two blower cards — each a solvable problem, together a weekend.

If your daily models are the 27B–35B class at Q4/Q5 — which covers most of what people actually run, per our 32GB VRAM model guide — the 5090 delivers a materially better experience every single day. Whether that experience is worth $4,329 against its own $1,999 MSRP is a question we’ve been sour on all year, but against dual R9700s specifically: for sub-32GB workloads, yes, the 5090 is the better tool. You’re paying ~$1,200 extra for 1.5× decode, 3× prefill, and zero ecosystem friction.

Where dual R9700s win

One place, decisively: the 33–64GB model window. The 5090’s score in that window is zero tokens per second, because the model doesn’t load.

The question is whether what you get there is worth having. Be honest about 11.49 tok/s on a dense 70B: that’s reading-pace for chat, workable for single-turn Q&A and batch jobs you walk away from, and frustrating for agentic loops that chew through thousands of tokens per step. It is, however, fully local, fully on-GPU, and it comes with ~600 tok/s prefill — so a 70B doing RAG over long documents is genuinely usable, because the expensive part of that workload is ingesting context, not streaming the answer.

The stronger case in the same window is big MoE models. The 2026 open-weight wave is increasingly sparse-active: total parameters set the VRAM bill, but only a few billion active parameters set the decode speed — precisely the profile that rewards cheap capacity over expensive bandwidth. The shootout included Qwen3.5 122B MoE for exactly this reason: at quantizations that squeeze under 64GB, that class of model is dual-R9700 territory or nothing on new consumer-adjacent hardware, and it decodes far faster than a dense 70B because so little of it is active per token.

There’s also a pragmatic argument the spec sheet misses: you don’t have to buy both cards at once. A single R9700 at $1,299–$1,600 is already a competent 32GB card — 127–163 tok/s on 35B MoE models. Add the second card when a model you care about demands it. The 5090 is a single all-or-nothing purchase; the R9700 path amortizes.

Power is closer to a wash than the card count suggests: 600W total board power for the pair versus 575W for the 5090, and the R9700s only pull their combined maximum when both are actually loaded — during decode, a layer-split pair alternates rather than saturates. Either setup wants a quality 1,000W+ PSU.

The decision, by buyer

Buy the RTX 5090 if you live in the 7B–35B class and use it hard: high-volume agentic coding (where it becomes a serious local backend for Cline or Cursor), long-context prefill all day, image and video generation where CUDA support is non-negotiable. You’re buying the best sub-32GB experience available and paying monopoly rent for it.

Buy dual R9700s if 48–70B-class models are the mission — you want a dense 70B or a 100B+ MoE running on your own hardware, you’re comfortable in Linux, and you’ll follow an open-source ROCm inference stack through its rough edges. You get 2× the VRAM of a 5090 for ~$1,200 less and give up raw speed on everything smaller.

Buy one R9700 if you’re anywhere in between — it’s the best new-hardware value per GB on the market, and it leaves the 64GB door open.

Buy neither if you’d use the 70B tier a few hours a month. Renting a cloud GPU on RunPod covers occasional big-model work for single-digit dollars per session, and our rent-vs-buy math says occasional use never amortizes a $3,000+ purchase.

FAQ

Can I just add a second RTX 5090 for 64GB? Mechanically yes — llama.cpp splits across them the same way. But that’s $8,600+ at August 2026 pricing and ~1,150W of GPU, versus $3,200 and 600W for the R9700 pair, to reach the same capacity tier. Dual 5090s only make sense if you also need their prefill speed at 64GB scale.

Do dual R9700s need NVLink or a bridge? No such thing exists for them — they communicate over PCIe, which is fine for layer-split inference. Give each card real lanes (x8/x8 PCIe 5.0 or better) and skip x1 risers.

Is a used RTX 3090 pair the cheaper 48GB version of this? Two used 3090s (~$2,100–$2,500 total) get you 48GB with CUDA and more per-card bandwidth — still the value path if 42GB-class models are your ceiling and you’ll accept mining-era silicon with no warranty. The R9700 pair counters with 64GB, warranties, blower coolers that stack cleanly, and about 100W less combined board power.

Will the 70B speed on dual R9700s improve? Some. The single-card MoE record went from 127 to 163 tok/s in five months of driver work, and dense-model decode is bandwidth-bound with less headroom — expect incremental gains, not a doubling. At 640 GB/s per card, a 42.5GB model has a hard theoretical ceiling around 15 tok/s regardless of software.

Recommended Gear

• NVIDIA RTX 5090 — the sub-32GB speed king, if you can stomach the street price
• AMD Radeon AI PRO R9700 — 32GB per card, the cheapest new-hardware path to 64GB

Sources

• A LocalLLaMA benchmark maps where RTX 5090, AI395, and dual R9700 actually win — Insights (llama-bench build 8463 shootout writeup)
• RTX 5090 (CUDA) vs R9700 (Vulkan), Qwen3.5-35B-A3B llama-bench: 7,026 vs 2,713 pp / 194 vs 127.4 tg — llama.cpp discussion #19890
• RDNA4 Llama experiments: dual-R9700 tuning, 115→163.7 tok/s MoE decode — llama.cpp discussion #21043
• Dual R9700 ROCm benchmarks: 2× prefill scaling, flat decode (ROCm 7.2.2, Ubuntu 26.04) — GitHub
• Radeon AI PRO R9700 retail launch at $1,299 — TechPowerUp
• R9700 partner cards from $1,244 — TechRadar Pro
• R9700 in-stock listings $1,522–$2,139, August 2026 — GPU Poet
• Gaming GPU prices 2026: RTX 5090 ~$4,329 on Amazon — Tech Insider
• Llama 3.3 70B Instruct Q4_K_M GGUF, 42.5GB — bartowski, Hugging Face

Last updated August 17, 2026. Prices and specs change; verify current rates before purchasing.