AMD Radeon AI PRO R9700 32GB AI Performance Review

AMD Radeon AI PRO R9700 32GB AI Performance Review

Final Thoughts

The AMD Radeon AI PRO R9700 32GB kept our 27B model fully in VRAM for the fastest replies, at about a third of an RTX 5090's price, and two cards cost less than one.

Pros

32GB of VRAM for about a third of the price of an RTX 5090

Very steady power draw

Dual-slot blower for dense applications

Games at RX 9070 XT level

Cons

Noisy blower cooler

Priced out of consumer segment

8.8

Rating

Introduction – AMD Radeon AI PRO R9700 32GB

The AMD AI PRO R9700 32GB is a 300W RDNA 4 card with 32GB of GDDR6 aimed at running AI models locally. According to AMD Philippines, the R9700 is available from partners for roughly PHP130,000 to PHP135,000 as of writing. The only GeForce card with the same 32GB of memory, the RTX 5090, lists between PHP372,000 and PHP430,000 at local retailers as of September 19, 2026. We put the R9700 through our new local AI test bench on Ubuntu Linux alongside two 16GB cards, the GeForce RTX 5070 Ti and the Radeon RX 9070 XT, and measured every test the way we measure everything else on this bench: with in-line power captured at the connector, second by second, and hope to show you a new dimension in analyzing performance in ways that most miss.

Closer Look

PowerColor’s version follows AMD’s reference layout for the R9700: a dual-slot card with a blower-style cooler that pushes heat out through the rear bracket, powered by a single 16-pin 12V-2×6 (12VHPWR on AMD! gasp) connector. Blower coolers exhaust hot air out of the case instead of into it, which is why workstation cards use them when several are stacked side by side. On our bench, the blower never stopped spinning, holding 20% fan speed at idle, where both 16GB cards parked their fans.

The unit we tested is a short-term loan that AMD Philippines pulled from its retail demo pool for its AI promotions. The card has been returned as of this writing.

• Compute: 64 compute units, 4,096 stream processors and 128 AI accelerators, with a boost clock of up to 2,920 MHz.
• AI throughput (AMD rated): 383 TOPS INT8, 766 TOPS INT4 and 191 TFLOPS FP16 matrix performance.
• Memory: 32GB GDDR6 with 64MB of Infinity Cache. ECC is supported on Linux only.
• Power and interface: 300W total board power, PCIe 5.0 x16, with a 750W minimum PSU recommendation from AMD.
• Outputs: Up to four DisplayPort outputs.

Full specifications are listed on AMD’s Radeon AI PRO R9700 product page.

Our Test Bench

• CPU: AMD Ryzen 7 9800X3D
• Cooler: CoolerMaster V8 ACE 3DHP
• Motherboard: GIGABYTE X870E AORUS MASTER (BIOS F14c)
• Memory: Kingston FURY Renegade DDR5-7200 48GB kit (2x24GB), running XMP profile 3 at DDR5-6000 CL32
• Storage: Kingston FURY Renegade G5 2TB SSDs (two drives: 1 holding the AMD software image, 1 holding the NVIDIA image, so each vendor runs on its own clean install)
• Power supply: FSP Vita GM 1000W
• Power measurement: ElmorLabs BenchLab
• Chassis: OpenBenchTable
• Software: Ubuntu 24.04.4 LTS (kernel 6.17.0-14), ROCm 7.2.4 on the Radeon, NVIDIA driver 595.91.07 with CUDA 13.2 on GeForce, PyTorch 2.14.0, and llama.cpp build 11046

How We Test

Its so refreshing to do GPU testing on Linux as everything can be done via scripts. So in this review, every test follows the same pattern: 60 seconds idle, 10 minutes of continuous load, then 60 seconds of idle again. We report the steady part of the load, starting 60 seconds after the work begins, so warm-up does not skew the numbers. To keep things consistent, every card runs the same model files, prompts and settings, and each card ran the 27B chat test twice to confirm the results repeat. On the R9700, the two runs finished 0.1% apart on speed and 0.6% apart on power.

Most AI reviews stop at token generation performance, reporting tokens per second. We log power the whole way through, because it tells you what a card costs to run, how hard it works for each answer, and how evenly it draws from your PSU. The models below I’ve chosen because this is what I’m playing with for consumer hardware recently; I expect this to change in the next few months for Q3-Q4 2026, but they should hold up.

• Text models: Qwen3.8-27B (UD-IQ4_XS) and Qwen3.5-9B (Q4_K_M), with a 32,768-token context window, flash attention on, and a fixed-seed greedy sampling.
• Image model: Stable Diffusion XL 1.0 base in ComfyUI, 1024×1024, 30 steps, Euler sampler, batch size 1. Typical out-of-the-box stuff.
• GPU power: the BenchLab GPU power channel, which covers the card’s power connectors.

Important: Power is reported directly per component except the system draw where combined power is measured. None of these are measured from the AC, so efficiency computation is not taken into consideration.

Idle and Standby Power

We kick off with idle readings. GPU power with nothing running, and with a model loaded in VRAM, waiting for a request. In most applications where a local AI box or home server spends most of its day waiting, standby power decides what it costs to leave a model ready around the clock.

The R9700 idled at 15.7W and settled at 17.9W with either model loaded. The RTX 5070 Ti sat at 15.6W idle and 16.0W with the 27B loaded. The RX 9070 XT idled lowest at 10.9W, rising to 14.5W with a model in memory. Whole-system power at idle was 82W for the R9700, 77W for the RTX 5070 Ti, and 82W for the RX 9070 XT. The R9700’s blower ran at 20% throughout, keeping the GPU at 31C, about 10C cooler than the two fan-stop cards.

Chat Performance

At its most basic, AI chat models are very good at doing that and it is centrally the primary premise of where most of these models work best at. In this test we have a fixed question, a 512-token answer, repeated for 10 minutes then we report on decode speed is how fast the answer is written, in tokens per second. You’ll see this mostly in private ChatGPT-style assistant running on your own PC, drafting emails, answering questions or explaining documents without sending anything to the cloud.

On the 27B model, the R9700 generated 31.7 tokens per second, 4.7% ahead of the RTX 5070 Ti at 30.3 and 16.4% ahead of the RX 9070 XT at 27.2. All three write faster than most people read. On the 9B model, the RTX 5070 Ti led at 124.1 tokens per second, 48.7% ahead of the R9700 at 83.4, with the RX 9070 XT at 84.2. The first word appeared within 0.21 seconds on the R9700, 0.20 seconds on the RTX 5070 Ti and 0.23 seconds on the RX 9070 XT.

The 9B result follows memory bandwidth. Writing each token means reading the model’s weights from VRAM, and the RTX 5070 Ti’s 896 GB/s is 40% more than the 640 GB/s on both Radeon cards, which share the same bandwidth and land within 1% of each other. By that logic the RTX 5070 Ti should also win on the 27B model. It does not, and our power logs show why.

At a 32,768-token context, the 27B model plus its working memory needs about 15GB of VRAM, and llama.cpp keeps a safety margin free. On the 16GB cards it could not fit everything, so it moved part of the model to the CPU: 4 of 66 layers on the RTX 5070 Ti and 2 of 66 on the RX 9070 XT. The R9700 held all 66 layers with about 17GB to spare. BenchLab caught the result on the CPU rail. Between the 9B and 27B chat tests, CPU power rose by 32.6W on the RTX 5070 Ti and 14.4W on the RX 9070 XT, and by 1.2W on the R9700. The RTX 5070 Ti’s GPU sat at 56% utilisation and 197W, waiting on the CPU for its share of every token.

This is the R9700’s 32GB at work with an ordinary model and an ordinary context size. A card with 29% less memory bandwidth than the RTX 5070 Ti still wrote the 27B answers fastest, because nothing had to leave VRAM.

The power traces show three different behaviours. The R9700 climbs straight to its 300W limit and stays there, averaging 305W with a standard deviation of 9.8W. The RTX 5070 Ti holds a flat 197W, well under its own 300W limit. The RX 9070 XT averages 247W with the smallest swing of the three at 6.6W.

Running at its full limit costs the R9700 on efficiency. It used 9.75 joules of GPU energy per token on the 27B model, against 6.58 on the RTX 5070 Ti and 9.17 on the RX 9070 XT. The GPU-only figure flatters the 16GB cards on the 27B test, because their CPUs did extra work, so we also divided whole-system power by speed:

Counted at the whole-system level, the RTX 5070 Ti remains the most efficient chat card here, and the R9700 moves ahead of the RX 9070 XT on the 27B model.

Long Prompts

Ok here’s where we get a bit more specific. Long prompt means we include references and more data in our prompt, and in this test we show how fast each card reads long, document-sized prompts before it starts answering, in prompt tokens per second. This is typically how pasting a long PDF, a contract or a chunk of code and asking for a summary would look like in application. We report prompt speed decides how long you wait before the first word appears.

Prompt reading is compute-heavy, and the RTX 5070 Ti leads both models: 1,568 tokens per second on the 27B against 1,198 on the R9700 and 1,203 on the RX 9070 XT, and 5,438 on the 9B against 3,450 and 3,521. The two Radeons finish within 2% of each other on both models.

This is where the power behaviour differs largely. The RTX 5070 Ti swings between reading and writing phases, averaging 261W, deviating by about 59.5W with P1% of reads above 342W. The R9700 holds 306W with a stdev of 8.6W, pinned to its limit whether it is reading or writing. The RX 9070 XT sits in between at 270W with a 16.9W swing.

Agent Workload Test

Agentic operation is where a lot of focus in development in AI right now lies and where end-users can really start understanding the benefits of having local AI when they have a usecase where it matters. Repeated long tool-style prompts followed by short replies, the pattern coding assistants and AI agents produce as they read files, call tools and respond, those kinda things. This is pretty common where a local model powering a coding assistant in your editor, or an agent that works through a task step by step on your own machine.

On the 27B model the R9700 wrote replies fastest at 30.6 tokens per second, ~8% ahead of the RTX 5070 Ti at 28.3 and ~15% ahead of the RX 9070 XT at 26.4. On the 9B model the RTX 5070 Ti led at 117.1 tokens per second, against 79.8 on the R9700 and 80.3 on the RX 9070 XT.

The agent traces show each card’s power management most clearly. Every time a long prompt arrives, the RTX 5070 Ti jumps from about 200W to between 300W and 340W, then drops back as it writes the reply. It averaged 200W with a 39.8W standard deviation and a 99th percentile of 338W. The R9700 barely reacts to the change in work, averaging 304W with its 99th percentile at 325W, dipping only in the short gaps between requests. The RX 9070 XT averaged 250W with smaller spikes, peaking at 292W in the 99th percentile.

For anyone sizing a power supply, the RTX 5070 Ti’s average understates what it asks for. Its short peaks on this workload ran about 140W above its mean.

Image Generation

Including this one here just for comparison, but I’m still working on a deeper image-gen and video-gen benchmark. For now, Stable Diffusion XL images at 1024×1024, generated one after another for 10 minutes is a standard out-of-box test which should be relatable for users making thumbnails, concept images, or product mockups locally or whatever trend kids are into these days.

The RTX 5070 Ti produced 9.84 images per minute, the R9700 9.03 and the RX 9070 XT 8.22. The R9700 finished 8.2% behind the RTX 5070 Ti and 9.9% ahead of the RX 9070 XT.

Per image, the RTX 5070 Ti used 0.486Wh of GPU energy, the RX 9070 XT 0.523Wh and the R9700 0.549Wh.

Image generation is the one workload where all three cards swing hard, with standard deviations between 43.7W and 47.2W. Each dip in the trace is the pause between one finished image and the next.

Overall Power Analysis

Across every workload, the R9700 behaves like a card tuned to use its full power budget. The RTX 5070 Ti draws what each moment of work needs, and the RX 9070 XT sits between the two. We start with a distribution chart below of the cards we tested, the smaller the bar the tighter the draw is, meaning less power swings. This is mostly affected by the actual workload rather than the card itself.

• Radeon AI PRO R9700: Averaged 304W to 307W in all six text workloads and hit its power limit in 99% to 100% of samples. Its highest 99th percentile reading was 348W, on the 9B agent test.
• GeForce RTX 5070 Ti: Ranged from 197W to 313W on average depending on the workload, with the widest swings on long prompts and agent work.
• Radeon RX 9070 XT: Averaged 247W to 285W on text workloads, with the steadiest draw of the three on 27B chat.

At the whole-system level, the busiest test pushed our bench to 433W on the RX 9070 XT, 399W on the RTX 5070 Ti and 395W on the R9700. AMD’s 750W minimum PSU recommendation for the R9700 leaves plenty of headroom on a single-card build like ours.

The R9700’s blower held the GPU between 62C and 65C at full power in an open-air bench, running up to 72% fan speed on the long-prompt tests. We did not measure noise.

What 32GB Costs

Between the two 32GB cards, the R9700 costs about a third of the RTX 5090 per gigabyte. The RX 9070 XT is the cheapest memory per gigabyte here, but our 27B chat test already pushed it past 16GB into CPU offload. The RTX 5090 has 1,792 GB/s of memory bandwidth, 2.8 times the R9700’s, and we would expect it to write text faster on any model both cards hold. We have not tested it, and its cheapest local listing costs at least PHP237,000 more than the R9700.

Gaming Performance

The AI PRO R9700 uses the same Navi 48 chip as the Radeon RX 9070 XT, so here’s a quick shootout of some games to see whether it can double as a gaming card. We ran the R9700 on the regular Adrenalin 25.20.42.14 driver in Windows 11. All three games ran at 2560×1440 on the highest preset, and we captured 60 seconds of each with CapFrameX in typical Back2Gaming granular style. Cyberpunk 2077 is our main GPU load but we sprinkled in Counter-Strike 2 and DotA 2 there for esports players, who care more about frame rates and lows than eye candy.

Cyberpunk 2077

The R9700 averaged 127.3 FPS against the RX 9070 XT’s 128.3 FPS, a 1 FPS gap… that’s a rounding difference. Same goes for the P1 and P0.1.

Besides some random spikes typical of single runs, both card are practically even all throughout the run. Cyberpunk used about 8.4GB of VRAM on the R9700 at these settings. That leaves more than 23GB unused, so the extra memory does nothing for gaming at 1440p. The RX 9070 XT can take on some 4K games but any game tha can take advantage of that RAM size, they will surely run much smoother in same case scenarios versus the gaming card.

Counter-Strike 2

I’m including these esports games just to nullify the VRAM effect further just to show how even the GPUs are.

DOTA 2

Dota 2 is the one result that breaks from the others. The RX 9070 XT averaged 210.9 FPS and the R9700 averaged 170.7 FPS, a 24% gap, with the lows following the same pattern. Given that both cards run the same chip and matched each other within about 2% in the other two games, we attribute most of this gap to the different test sessions, most likely a game patch or the driver change, rather than to the card. The R9700 still averaged over 170 FPS, with 1% lows of 96.3 FPS at the highest settings.

If you already own an R9700 for AI work, you can game on it after hours and get RX 9070 XT-class frame rates at 1440p. Flip that around with the R9700 and immediately, it doesn’t hold: at PHP130,000 to PHP135,000 against PHP47,000 to PHP56,000 for an RX 9070 XT, nobody should buy the R9700 for gaming first. Its blower cooler is also louder than the open-air coolers on most RX 9070 XT models.

Final Thoughts

As a single card, AMD’s AI PRO R9700 is realistically a compromise for applications where you can use the 32GB VRAM space. This is wholly dependent on usage, and AMD’s economical advantage makes it a viable option for fine-tuning and LLM applications. Its true value shines best when you stack them up and easily scaling up to 128GB. Barring HEDT platform cost, its hard to beat the accessibility of the Radeon at that scale versus the cost of a similar CUDA stack.

Card for card, NVIDIA is the safer buy for consumer-class testing and learning. At PHP75,000 to PHP90,000, the GeForce RTX 5070 Ti beat the AMD Radeon AI PRO R9700 32GB by 49% on 9B chat, read long prompts faster on both models, generated images faster, and used less energy per token in every text test. The R9700 won the 27B chat and agent tests only because both 16GB cards had to push part of that model onto the CPU, a consumer 1 at that.. If single-card throughput is what you are buying, our data points to CUDA, and we expect an RTX 5090 to widen that lead, if you can afford it.

Again, the AMD Radeon AI PRO R9700’s case is economics, and it grows with every card you add. Two R9700s cost roughly PHP260,000 to PHP270,000, at least PHP102,000 less than the cheapest RTX 5090 we found locally, and give you 64GB of VRAM against the RTX 5090’s 32GB. That extra memory buys precision: room to run the 27B model we tested at 8-bit instead of 4-bit, or to step up to larger models, with context to spare. On a workstation CPU and board with the PCIe lanes for four cards, the same approach reaches 128GB. Worth noting is that we skipped workstation graphics test in this one simply because the price bracket and positioning didn’t put it in that category. We will cover this in a follow-up article primarily.

Back to the actual tests we did, we used one card in this shootout, so we can only hypothesize multi-card speed. When a model is split across cards by layers, the common setup, a second card adds memory more than it adds speed for a single chat. The capacity math sticks here irregardless, and it shows most clearly using US guesstimate prices, since NVIDIA’s 96GB RTX PRO 6000 is not sold in the Philippines. According to recent US listings, the R9700 goes for about USD 1,600 and the cheapest RTX 5090 for about USD 5,000, while NVIDIA now lists the RTX PRO 6000 at USD 16,000. That is roughly USD 50 per gigabyte of VRAM on the R9700, against USD 156 on the RTX 5090 and USD 167 on the RTX PRO 6000. Two R9700s give you 64GB for about USD 3,200, four give you 128GB for about USD 6,400, and even seven, at 224GB, cost less than one 96GB RTX PRO 6000.

So yeah, wrapping things up, one final detail is power. The Radeon AI PRO R9700 draws its 300W limit through almost every AI workload we ran, so each card you add is roughly another 300W of steady draw. Our single-card bench measured 395W under load, which puts a two-card build near 700W before headroom, and a four-card workstation past what a single 1000W supply should carry.

For anyone whose local AI work is limited by memory rather than speed, the R9700 is the lowest-priced 32GB card we found locally, and the most direct way we know of to build toward 64GB on consumer setups and beyond for HEDT.

Check the latest price and availability of the AMD Radeon AI PRO R9700.

AMD Radeon AI PRO R9700 32GB AI Performance Review

Final Thoughts

The AMD Radeon AI PRO R9700 32GB kept our 27B model fully in VRAM for the fastest replies, at about a third of an RTX 5090's price, and two cards cost less than one.

Pros

32GB of VRAM for about a third of the price of an RTX 5090

Very steady power draw

Dual-slot blower for dense applications

Games at RX 9070 XT level

Cons

Noisy blower cooler

Priced out of consumer segment

8.8

Rating