Two AMD Radeon AI PRO R9700 cards run a 70B model at Q4 fully on-GPU at about 11.49 tok/s generation and roughly 600 tok/s prompt processing under ROCm, per RunAIHome's account of an August 2026 r/LocalLLaMA llama-bench shootout. The pair's 64 GB of VRAM holds a 70B Q4_K_M model (about 42.5 GB) with room to spare (RunAIHome's R9700 guide). A PowerColor R9700 listed at $1,835.99 on Amazon on October 16, 2026 (PriceHistory), so two cards cost about $3,672, roughly what one 48 GB Radeon PRO W7900 costs ($3,839 per SpecPicks' tracked Amazon listing on October 12, 2026). That single W7900 generates Llama 3.1 70B at Q4_K_M at 11.35 tok/s in llama.cpp (llm-tracker.info), while a 96 GB RTX PRO 6000 Blackwell reaches 34.05 tok/s on a Llama 3.3 70B Q4_K_M model (llm-tracker.info). On smaller models, a second R9700 doubles 32k-token prompt processing on Qwen 3.6 27B (611 to 1,216 tok/s) but leaves single-user generation flat (24.85 vs 24.31 tok/s) under a llama.cpp layer split (truelies444 benchmark repo).
What changed since September 2026: a dual-R9700 70B Q4 result of 11.49 tok/s generation and about 600 tok/s prompt processing is now cited (RunAIHome), and the per-card price is updated to $1,835.99 on Amazon as of October 16, 2026 (PriceHistory).
What changed since the April 2026 version
This guide first ran in April 2026. It has been rebuilt around published measurements, and five things changed.
- The first version's numbers are withdrawn. It reported "lab" measurements for a dual-R9700 rig, including 22.4 tok/s on Llama 3.3 70B, along with PSU clamp readings and thermal soaks. No source for those numbers could be found, and SpecPicks does not run a lab for this build. Every figure below links to its source.
- Launch facts are corrected. AMD's R9700 workstations became available on July 23, 2025, not in December 2025 (AMD). The card is 32 GB GDDR6 at 640 GB/s with a 300 W board power, not 270 W (RunAIHome; Puget Systems).
- Dual-card results are now public. Puget Systems, an independent llama.cpp benchmark repo and one owner's build log all published dual-R9700 numbers between August and September 2026 (details below).
- vLLM is the fast path on two cards. On Qwen 3.6 27B, a tuned vLLM setup with multi-token prediction reached 62.75 tok/s for a single user. llama.cpp reached 23.40 tok/s on the same pair (Puget Systems).
- Prices are live. The parts table below reads each part's current Amazon price. The first version quoted April UK prices.
October 2026 additions:
- New 70B comparison table. The 70B comparison table puts five published 70B Q4 results next to the dual-R9700 build: the Radeon PRO W7900 at 11.35 tok/s (llm-tracker.info), two RX 7900 XTX cards at 13.37 tok/s (1337hero), a Mac Studio M3 Ultra at 14.08 tok/s (geerlingguy/ai-benchmarks), two RTX 4090s at 19.06 tok/s (XiongjieDai) and the RTX PRO 6000 Blackwell at 34.05 tok/s (llm-tracker.info).
- Radeon PRO W7900 added as the same-money alternative. One 48 GB card with 864 GB/s of memory bandwidth and a 295 W board power (AMD), listed at $3,839 on SpecPicks' tracked Amazon listing (October 12, 2026).
- RTX PRO 6000 Blackwell added as the upper bound. 96 GB of GDDR7 at 1,792 GB/s and up to 600 W (NVIDIA).
- Opening rewritten around the W7900 and RTX PRO 6000 70B figures, and the two-card cost restated from RunAIHome's September street-price range.
- First dual-R9700 70B figure (October 17 update). RunAIHome's August 17, 2026 comparison reports a dual-R9700 box running a 70B Q4 model (Qwen2.5 70B) at 11.49 tok/s generation and roughly 600 tok/s prompt processing under ROCm, from an r/LocalLLaMA llama-bench shootout (RunAIHome). It is a secondhand report of a community run, and the exact quant file is not stated.
- Price refreshed (October 17 update). The PowerColor R9700 listed at $1,835.99 on Amazon on October 16, 2026 (PriceHistory), inside RunAIHome's September $1,400–$1,900 range.
Why dual R9700 is the budget 64 GB answer
The R9700 is AMD's RDNA 4 workstation card. It carries 32 GB of GDDR6 at 640 GB/s behind a dual-slot blower cooler designed to stack in multi-GPU chassis (RunAIHome). Two of them hold 64 GB. By RunAIHome's count, that is the cheapest new-hardware way to run a dense 70B Q4 model fully on-GPU. At September street prices of $1,400 to $1,900 per card, two cards cost $2,800 to $3,800. As of October 16, 2026, a PowerColor R9700 listed at $1,835.99 on Amazon (PriceHistory), near the top of that range.
The comparison point is the RTX 5090. It also has 32 GB, but its GDDR7 runs at 1,792 GB/s (TechPowerUp), nearly three times the R9700's bandwidth. For models that fit in 32 GB, a single 5090 is the faster card. The dual-R9700 case rests on capacity: a model too large for one 32 GB card fits across two without spilling to system RAM.
What do two R9700s actually do on current models?
Three independent sources published dual-R9700 results in August and September 2026. They agree on one pattern. A second card adds capacity and long-prompt speed. It does not add single-stream decode speed unless the software splits each layer across both cards (tensor parallelism).
Layer split in llama.cpp (ROCm 7.2.2, Ryzen 9 7900X), per the truelies444 benchmark repo:
| Model (quant) | Test | One R9700 | Two R9700s |
|---|---|---|---|
| Qwen 3.6 27B (Q5_K_M) | 32k prompt processing | 611 tok/s | 1,216 tok/s |
| Qwen 3.6 27B (Q5_K_M) | generation (tg128) | 24.85 tok/s | 24.31 tok/s |
| Gemma 4 31B (Q5_K_M) | 32k prompt processing | 425 tok/s | 840 tok/s |
| Gemma 4 31B (Q5_K_M) | generation (tg128) | 21.74 tok/s | 21.18 tok/s |
| Qwen 3.6 35B A3B MoE (Q5_K_M) | 32k prompt processing | 1,637 tok/s | 3,038 tok/s |
| Qwen 3.6 35B A3B MoE (Q5_K_M) | generation (tg128) | 77.25 tok/s | 71.88 tok/s |
vLLM tensor parallel vs llama.cpp on Qwen 3.6 27B, two cards, per Puget Systems (published August 2026, updated September):
| Setup | 1 user | 8 concurrent users |
|---|---|---|
| Stock vLLM, FP8, TP=2 | 15.88 tok/s | 156.21 tok/s |
| Tuned vLLM + MTP | 62.75 tok/s | 320.23 tok/s |
| llama.cpp Q4_K_M | 23.40 tok/s | 56.00 tok/s |
One owner's build log covers a Ryzen 9 9950X with two ASRock Creator R9700s and a 1,000 W PSU running Qwen 3.8 27B (Alex K. Miller, September 7, 2026). Generation was 19.2 tok/s on llama.cpp defaults (Q8_0). A gfx1201 build with tensor split raised it to 29.7 tok/s. A vLLM MXFP4 setup reached 184.9 tok/s.
None of the three sources publishes a dual-R9700 generation figure for a dense 70B model. The one published 70B number comes secondhand: RunAIHome reports a dual-R9700 box running a 70B Q4 model at 11.49 tok/s generation and roughly 600 tok/s prompt processing under ROCm in an August 2026 r/LocalLLaMA llama-bench shootout (RunAIHome). That fits the layer-split pattern above: the model fits, and its decode speed depends on the backend and on how the layers are split.
How does a dual R9700 compare with other 48 GB+ setups on a 70B model?
The only dual-R9700 70B figure is a secondhand community result reported by RunAIHome, so the useful comparison is with the other ways to hold a 70B Q4 model in GPU or unified memory. Each row below is a single-user llama.cpp or Ollama result for a 70B model at 4-bit (Llama-family, except the Qwen2.5 70B dual-R9700 row), taken from the linked source.
| Setup | Memory | Model (quant, runtime) | Generation | Prompt processing | Source |
|---|---|---|---|---|---|
| 1× Radeon PRO W7900 | 48 GB GDDR6 | Llama 3.1 70B Q4_K_M, llama.cpp ROCm | 11.35 tok/s | 297.53 tok/s (pp512) | llm-tracker.info |
| 2× Radeon RX 7900 XTX | 2× 24 GB | Llama 3.1 70B Q4_K_M, llama.cpp ROCm 7.1.1 | 13.37 tok/s | 341.41 tok/s (pp512) | 1337hero |
| Mac Studio M3 Ultra (512 GB) | unified | Llama 3.1 70B, Ollama default build | 14.08 tok/s | not published | geerlingguy/ai-benchmarks |
| 2× GeForce RTX 4090 | 2× 24 GB | Llama 3 70B Q4_K_M, llama.cpp | 19.06 tok/s | 905.38 tok/s (1,024-token prompt) | XiongjieDai |
| 1× RTX PRO 6000 Blackwell | 96 GB GDDR7 | Llama 3.3 70B fine-tune (Shisa V2) Q4_K_M, llama.cpp | 34.05 tok/s | 1,793.76 tok/s (pp512) | llm-tracker.info |
| 2× Radeon AI PRO R9700 | 2× 32 GB | Qwen2.5 70B Q4, llama-bench ROCm (community run, reported secondhand) | 11.49 tok/s | ~600 tok/s | RunAIHome |
Three things stand out.
- The W7900 is the closest like-for-like. It is the same vendor, the same ROCm stack and about the same money as two R9700s, but it has 48 GB on one card instead of 64 GB across two. Its 864 GB/s of bandwidth (AMD) is higher than one R9700's 640 GB/s (RunAIHome), and its 11.35 tok/s on a 70B model is the most direct public hint at what an RDNA workstation card does on this workload.
- Layer split caps decode at one card's bandwidth. Under a llama.cpp layer split, each token still streams the full ~42.5 GB of weights, one card after the other. At 640 GB/s per R9700 that sets a theoretical ceiling of about 15 tok/s (640 ÷ 42.5) before any overhead. That is arithmetic from the cited specs, not a measurement; the reported 11.49 tok/s community result (RunAIHome) sits below it. Beating it takes tensor parallelism, which is why Puget's tuned vLLM result on a 27B model is far ahead of its llama.cpp result.
- The RTX PRO 6000 is the speed answer, not the budget one. It holds 96 GB on one card at 1,792 GB/s and up to 600 W (NVIDIA), and its 34.05 tok/s is roughly three times the W7900's 70B figure. It is a single-card workstation part priced well above a dual-R9700 build.
Buy the parts: current prices
One listing per part. The price is the Amazon price read in the last 24 hours, when there is one. The platform mirrors the owner build cited above. The cart adds one R9700, so set the quantity to 2 on Amazon.
| Part | Pick | Why | Price | Buy |
|---|---|---|---|---|
| GPU (×2) | ASRock Radeon AI PRO R9700 Creator 32GB | The card in the cited owner build; dual-slot blower, 32 GB GDDR6 each | Unavailable on Amazon as of | Compare on eBay Search Amazon |
| Motherboard | MSI MPG X870E Carbon WiFi | AM5 board with two x16-length PCIe 5.0 slots for the pair | $386.99 (2026-10-30 PT) | View Current Price |
| CPU | AMD Ryzen 9 9950X | The 16-core CPU in the cited owner build | $645.99 (2026-10-31 PT) | View Current Price |
| Memory | G.SKILL Flare X5 64GB (2x32GB) DDR5-6000 CL30 | AMD EXPO kit; 64 GB of system RAM matches the 64 GB of VRAM for loading and staging models | $1,299.99 (2026-10-30 PT) | View Current Price |
| PSU | Corsair RM1000x ATX 3.1 | 1,000 W, as in the cited owner build: two 300 W cards plus a 16-core CPU | $179.99 (2026-10-31 PT) | View Current Price |
| Storage | Crucial T500 2TB | PCIe Gen4 NVMe, up to 7,400 MB/s rated read per its listing | $489.43 (2026-10-30 PT) | View Current Price |
Total for the 5 parts priced on Amazon in the last 24 hours: $3,002.39 (1 part has no current price and is not included).
🛒 Add this build to your Amazon cart (5 parts)Adds the 5 Amazon-priced parts above in one step; you review the cart on Amazon before buying. The other part is left out because Amazon has no current price for it; use its row above.
*Prices sourced from Amazon.com, read 2026-10-30 to 2026-10-31 PT (each row shows its own date). Price and availability subject to change.
DDR5 and R9700 prices have both risen since April. Check each row's live price rather than any older total.
How do you set up ROCm with dual R9700 in 2026? (step-by-step, gotchas)
The R9700 is gfx1201 (Navi 48). The benchmarks above ran on ROCm 7.2.2 (truelies444), and the owner build ran on Ubuntu 26.04 LTS (Alex K. Miller). Follow AMD's install guide for your distribution (ROCm documentation), add your user to the render and video groups, and confirm that rocminfo lists gfx1201 twice before building anything.
The cited sources report these gotchas:
- Build llama.cpp for gfx1201 and use tensor split. The default layer split left the hardware underused. The owner build went from 19.2 to 29.7 tok/s generation and from 590 to 1,458 tok/s prompt processing after rebuilding for gfx1201 with tensor split (Alex K. Miller).
- Load with
--no-mmap. Memory-mapped model loading did not work on that build and had to be disabled (Alex K. Miller). - Leave VRAM headroom in vLLM. The owner build needed GPU memory utilisation at 0.94 rather than 0.98 to avoid out-of-memory errors (Alex K. Miller).
- Tune vLLM before judging it. Stock vLLM FP8 with TP=2 was the slowest single-user result in Puget's test (15.88 tok/s), while the tuned setup with MTP was the fastest (62.75 tok/s) (Puget Systems).
When a second R9700 pays off, and when it does not
The published measurements point to a clear split.
A second R9700 pays off for:
- models between roughly 32 GB and 64 GB, such as 70B at Q4_K_M, which need about 42.5 GB (RunAIHome)
- long-context work such as RAG and whole-document prompts, where 32k-token prompt processing roughly doubled (truelies444)
- several concurrent users on vLLM, where aggregate throughput reached 320.23 tok/s across 8 users (Puget Systems)
A second R9700 does not pay off for:
- one-user chat on a model that fits one card. llama.cpp layer-split generation stayed flat or dipped slightly on every model in the truelies444 run.
- CUDA-only software stacks. The R9700 runs ROCm or Vulkan, so check your tools' ROCm support first.
- 32 GB-and-under models where raw speed matters. The RTX 5090's 1,792 GB/s (TechPowerUp) is the faster single-card option.
Bottom line
A dual Radeon AI PRO R9700 workstation is a 64 GB VRAM box. It holds 70B-class models at Q4 on-GPU, and it roughly doubles long-prompt processing. With a layer split it does not double single-user token generation; that takes tensor parallelism or a tuned vLLM setup. At the September 2026 street prices cited above, the two cards alone cost $2,800 to $3,800 (about $3,672 at the $1,835.99 Amazon listing of October 16, 2026, per PriceHistory), so this is no longer a sub-£2,000 build. Buy it for capacity and concurrency, not for faster single-user chat on models that already fit one card.
Citations and sources
- https://runaihome.com/blog/amd-radeon-ai-pro-r9700-local-ai-hardware-guide-2026/: R9700 MSRP, September 2026 street price, 640 GB/s, 300 W, blower cooler, 70B Q4_K_M memory need.
- https://www.amd.com/en/blogs/2025/amd-radeon-ai-pro-r9700-to-be-available-in-workstation.html: R9700 workstation availability, July 23, 2025.
- https://www.pugetsystems.com/labs/articles/amd-radeon-ai-pro-r9700-dual-gpu-ai-inference-performance/: dual-R9700 vLLM and llama.cpp results on Qwen 3.6 27B (August 2026, updated September 2026).
- https://github.com/truelies444/amd-radeon-ai-pro-r9700-llama-cpp-rocm-benchmarks: single vs dual R9700 llama.cpp results on ROCm 7.2.2.
- https://alexkmiller.com/blog/2026/09/07/dual-r9700-llama-cpp-and-vllm.html: dual-R9700 owner build, tensor-split and vLLM results, setup gotchas (September 7, 2026).
- https://www.techpowerup.com/gpu-specs/geforce-rtx-5090.c4216: RTX 5090 memory size and bandwidth.
- https://rocm.docs.amd.com/: ROCm installation documentation.
- https://llm-tracker.info/howto/AMD-GPUs: Radeon PRO W7900 Llama 3.1 70B Q4_K_M llama-bench results (pp512 297.53, tg128 11.35 tok/s).
- https://github.com/1337hero/rx7900xtx-llama-bench-rocm: dual RX 7900 XTX Llama 3.1 70B Q4_K_M results on ROCm 7.1.1 (pp512 341.41, tg128 13.37 tok/s).
- https://github.com/geerlingguy/ai-benchmarks: Mac Studio M3 Ultra 512 GB Llama 3.1 70B result (14.08 tok/s, 243 W).
- https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference: dual RTX 4090 LLaMA 3 70B Q4_K_M results (19.06 tok/s generation, 905.38 tok/s prompt eval).
- https://llm-tracker.info/RTX-PRO-6000: RTX PRO 6000 Blackwell Llama 3.3 70B (Shisa V2) Q4_K_M llama-bench results (pp512 1,793.76, tg128 34.05 tok/s).
- https://www.amd.com/en/products/graphics/workstations/radeon-pro/w7900.html: Radeon PRO W7900 48 GB, 864 GB/s, 295 W board power.
- https://www.nvidia.com/en-us/products/workstations/professional-desktop-gpus/rtx-pro-6000/: RTX PRO 6000 Blackwell 96 GB GDDR7, 1,792 GB/s, 600 W maximum.
- https://runaihome.com/blog/rtx-5090-vs-dual-amd-r9700-value-comparison-2026/: dual-R9700 70B Q4 result, 11.49 tok/s generation and about 600 tok/s prompt processing under ROCm, from an August 2026 r/LocalLLaMA shootout (published August 17, 2026).
- https://pricehistory.app/p/powercolor-amd-radeon-ai-pro-r9700-32gb-BFcRhGIm: PowerColor Radeon AI PRO R9700 32GB Amazon price, $1,835.99 on October 16, 2026.
This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.
