NVIDIA vs AMD GPUs for AI: 2026 Comparison and Recommendations
- Which is better for AI, NVIDIA or AMD?
- Key differences: software, memory, efficiency, price
- Consumer and workstation GPUs for local AI (October 2026 prices)
- How much VRAM do you need? Model-fit guide
- Data-center GPUs: H100, B200, MI300X, MI355X and what is next
- Benchmarks: what MLPerf says in 2026
- Market share and roadmap: Rubin vs Helios
- Why GPUs cost so much in 2026, and rent vs buy
- Best NVIDIA and AMD GPUs for AI (six picks)
- When to choose NVIDIA vs AMD
- AI personalization guide: Claude, ChatGPT, Gemini, Perplexity
- FAQ
- What changed in this update
- Sources and related reading
Which is better for AI, NVIDIA or AMD?
NVIDIA is the better default for most AI work in 2026; AMD is the better value in specific cases. NVIDIA's CUDA stack is still the one most AI frameworks, kernels and community tools target first. AMD's ROCm stack has improved and its hardware often offers more memory per dollar, but you should confirm support for your exact card, operating system and tools before buying.
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| Your situation | Better default | Why | Watch-out |
|---|---|---|---|
| Local LLMs (7B–32B) on a desktop | NVIDIA (RTX 3090/4090/5090); AMD R9700 if you want 32GB for less | CUDA tools work first; the R9700 gives 32GB at a lower list price than the RTX 5090 | ROCm support depends on card and OS; Linux is the smoothest path |
| AI image and video generation (ComfyUI, Stable Diffusion) | NVIDIA | Most community workflows and custom nodes assume CUDA | AMD can work for many pipelines but may need more troubleshooting |
| Fine-tuning small models (LoRA) | NVIDIA 24–32GB or AMD R9700 (32GB) | VRAM decides what fits; both brands offer 24–32GB options | Check framework and quantization-library support on AMD |
| Large-scale training | NVIDIA | NVLink systems plus CUDA are the broadest, most proven option | AMD says MI355X is within about 5–6% of B200 on two MLPerf training tasks (vendor-stated) |
| High-volume cloud inference | Test both | AMD offers 288GB per GPU on MI355X and reports competitive MLPerf inference results | Results vary by model, precision and scenario; test your own workload |
| Tight budget | Used RTX 3090/4090, or AMD RX 9070 XT / R9700 | Lower entry cost than a new RTX 5090 at 2026 street prices | Used cards carry warranty and wear risk |
Key differences: software, memory, efficiency, price
The biggest gap between NVIDIA and AMD for AI is software maturity, not raw hardware. The table below summarizes where each vendor leads as of October 2026.
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| Factor | NVIDIA | AMD |
|---|---|---|
| Software ecosystem | CUDA, cuDNN and TensorRT-LLM; the default target for most frameworks and community tools | ROCm 7.x, open source; supports PyTorch and llama.cpp, with Linux the best-supported route |
| Memory (consumer / workstation) | Up to 32GB GDDR7 (RTX 5090) | 32GB GDDR6 (Radeon AI PRO R9700); 16GB GDDR6 (RX 9070 XT) |
| Memory (data center) | H100 80GB HBM3; H200 141GB HBM3E; B200 about 180–192GB HBM3E; B300 up to 288GB HBM3E | MI300X 192GB HBM3; MI355X 288GB HBM3E; MI455X up to 432GB HBM4 (AMD-stated) |
| Power and efficiency | Generally stronger performance per watt in recent comparisons | MI355X is rated up to about 1,400W versus about 1,000W for a standalone B200, per one analysis, though AMD reports large efficiency gains over MI300X |
| Price per GB of VRAM | Highest at the top end; RTX 5090 street price is more than double its $1,999 list price | R9700 lists at $1,299 for 32GB; street price has run about $1,400–$1,900 |
| Market share (AI accelerators, 2026 est.) | About 75–85% of revenue, depending on methodology | About 5–7% of revenue |
Consumer and workstation GPUs for local AI (October 2026 prices)
In October 2026, GPU street prices sit far above list prices because of a memory shortage. Official MSRPs have not changed; the markup appears in retail and resale listings. Treat the street column as a range from price trackers, not a quote.
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| GPU | VRAM | List price | Street price (Sept–Oct 2026) | Power | Best for |
|---|---|---|---|---|---|
| NVIDIA RTX 5090 | 32GB GDDR7 | $1,999 | About $4,300–$5,000+ new; some marketplace trackers show higher | 575W | Fastest consumer CUDA card; large local models |
| NVIDIA RTX 4090 | 24GB GDDR6X | $1,599 (launch) | About $2,150–$2,350 used | 450W | Proven CUDA workhorse up to ~32B models at 4-bit |
| NVIDIA RTX 3090 | 24GB GDDR6X | $1,499 (launch) | About $1,250 used | 350W | Cheapest 24GB CUDA entry |
| AMD Radeon AI PRO R9700 | 32GB GDDR6 | $1,299 | About $1,400–$1,900 | 300W | 32GB for local LLMs on Linux/ROCm |
| AMD Radeon RX 9070 XT | 16GB GDDR6 | $599 | About $760–$830 | 304W | Image generation and small LLMs on a budget |
Price sources: GPU price trackers and press reports (see Sources). Used-card prices come from a September 2026 local-AI buying guide and move with the market.
How much VRAM do you need? Model-fit guide
VRAM, not brand, decides which models you can run on one GPU. A 4-bit quantized model needs roughly 0.5–0.6 GB per billion parameters, plus memory for context (the KV cache). These are our rule-of-thumb estimates; real usage varies by quantization format, context length and software.
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| Model size | Approx. 4-bit weights | 16GB (RX 9070 XT) | 24GB (RTX 3090/4090) | 32GB (RTX 5090, R9700) |
|---|---|---|---|---|
| 8B | ~5GB | Fits | Fits | Fits |
| 14B | ~9GB | Fits | Fits | Fits |
| 32B | ~19GB | Does not fit comfortably | Fits with modest context | Fits with more context |
| 70B | ~40GB | No | No | No: needs two GPUs, a larger card, or CPU offloading |
Correction note: an earlier version of this article listed single-GPU Llama 70B speeds for 24GB and 32GB cards. A 70B model at 4-bit does not fit in that much VRAM, so those figures were removed (see the update log).
Data-center GPUs: H100, B200, MI300X, MI355X and what is next
For data-center AI, NVIDIA's H100 remains the value workhorse while Blackwell (B200/B300) leads on performance; AMD's MI300X and MI355X compete mainly on memory capacity and price. Cloud rates below are per GPU-hour and vary widely by provider.
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| Accelerator | Memory | Status (Oct 2026) | Typical cloud rate per GPU-hour |
|---|---|---|---|
| NVIDIA H100 | 80GB HBM3 | Mature and widely available | Median about $3.50 on-demand; listings from about $1.30 to $12.29 |
| NVIDIA H200 | 141GB HBM3E | Available | About $4.43 average (Sept 2026 tracker) |
| NVIDIA B200 | About 180–192GB HBM3E (varies by SKU) | Available; often reserved | About $3.75–$6.39 at specialist clouds; hyperscalers higher |
| NVIDIA B300 / GB300 | Up to 288GB HBM3E | Shipping; used in MLPerf v6.0 submissions | Little public pricing |
| AMD Instinct MI300X | 192GB HBM3 | Available at several clouds | About $3.59–$4.98 |
| AMD Instinct MI355X | 288GB HBM3E | Shipping; limited cloud listings | One provider listed about $2.59 in July 2026 |
| NVIDIA Vera Rubin | HBM4 | Ramping in the second half of 2026 | No public rates yet |
| AMD Instinct MI455X (Helios) | Up to 432GB HBM4 (AMD-stated) | Launched July 23, 2026; shipments from Q3 | No public rates yet |
Benchmarks: what MLPerf says in 2026
On MLCommons' MLPerf results, AMD's MI355X has closed much of the gap with NVIDIA's Blackwell on selected LLM tasks, but the comparisons are mostly vendor-stated and scenario-dependent. Both vendors choose which tests to highlight, so read results as a map, not a leaderboard.
- MLPerf Training v6.0 (published June 16, 2026): AMD reports its MI355X came within about 5% of NVIDIA's B200 on Llama 2 70B fine-tuning and within about 6% on Llama 3.1 8B pre-training, using FP4 formats on both sides (AMD's MXFP4 vs NVIDIA's NVFP4). It was also AMD's first multi-node training submission. (AMD summary; analysis)
- MLPerf Inference v6.0: AMD says an eight-GPU MI355X node matched the B200 on Llama 2 70B offline, reached 97% in server mode and 119% in interactive mode, and beat the B200 by 11–15% on GPT-OSS-120B while trailing the B300. AMD also reported crossing one million tokens per second using up to 94 GPUs. (AMD summary; The Decoder)
- NVIDIA's scale record: NVIDIA's GB300 NVL72 submission reached about 2.5 million tokens per second across 288 GPUs, the largest scale in the benchmark's history, per press coverage of the v6.0 round.
- Takeaway: AMD is credible for several training and inference workloads, while NVIDIA keeps the lead in raw per-GPU speed, scale-out submissions and software breadth. Always test your own model, precision and batch size.
Why the old consumer benchmark table was removed: the previous version showed tokens-per-second and image-throughput figures for RTX 5090, RX 9070 XT, RTX 4090 and RX 7900 XTX without a named test setup. We replaced them with the sourced MLPerf results above and the VRAM-fit guide. For consumer cards, run llama.cpp or your own workflow on your hardware before you buy.
Market share and roadmap: Rubin vs Helios
NVIDIA still holds an estimated 75–85% of AI accelerator revenue in 2026, versus about 5–7% for AMD. Estimates vary because some analysts count hyperscaler custom chips, which one source puts at roughly 10–18%. NVIDIA's share is reported to have peaked near 87% in 2024. (Silicon Analysts)
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| Platform | What has been stated | Status |
|---|---|---|
| NVIDIA Vera Rubin | NVIDIA said on May 31, 2026 that Vera Rubin was ramping into full production, with production shipments starting in the fall; some trackers report early cloud shipments from July | Ramping in H2 2026 |
| AMD Instinct MI455X / Helios | Launched July 23, 2026; AMD guided first Helios shipments for Q3 2026, ramping into 2027 | Early shipments |
| AMD Instinct MI500 / Helios 500 | AMD has said MI500 is planned for 2027 | Roadmap |
| Consumer RTX 50 Super and RTX 60 | Reports and leaks say the RTX 50 Super refresh is delayed and RTX 60 may slip to 2028; NVIDIA has not confirmed these dates | Unconfirmed |
AMD customer commitments reported in 2026 include OpenAI (a 6 GW agreement announced in October 2025), Oracle (50,000 MI450-series GPUs starting in Q3 2026), Microsoft (Helios on Azure) and Anthropic (up to 2 GW, with the first gigawatt beginning in the first half of 2027). (Reuters via Yahoo Finance; Fierce Network) Commitments are not shipments, so watch delivery timing.
Why GPUs cost so much in 2026, and rent vs buy
RTX 5090 street prices of roughly $4,300 to $5,000+ reflect a GDDR7 memory shortage as memory supply is directed to AI data-center products. NVIDIA's official list prices are unchanged; the premium shows up in retail and resale. (price tracker; RTX 5090 price history; Tech Times)
Renting is often cheaper than buying for short projects. At the October 2026 median of about $3.50 per H100 GPU-hour, 100 hours of experiments costs roughly $350, while running one H100 around the clock costs about $2,500 per month. A $4,500+ RTX 5090 only pays off if you use it heavily for months.
- Buy used if you need CUDA and 24GB: a used RTX 3090 or 4090 costs far less than a new RTX 5090 but carries warranty risk.
- Consider AMD if you need 32GB on a budget and are comfortable with Linux and ROCm: the Radeon AI PRO R9700.
- Rent for 70B+ models, short training runs, or when you need 80GB+ of memory.
- Wait if you can: reports suggest relief is not imminent, so do not pay a premium for a card you will barely use.
Best NVIDIA and AMD GPUs for AI (six picks)
Our picks balance software support, VRAM and realistic 2026 availability. Prices are October 2026 snapshots.
1. Best overall: NVIDIA GeForce RTX 5090
- Why: The most VRAM of any consumer NVIDIA card, 21,760 CUDA cores, about 1.8 TB/s of memory bandwidth and the broadest CUDA tool support.
- Pros: Fastest consumer option for local LLMs, image and video generation; 32GB fits 32B-class models with long context.
- Cons: Street price is more than double the $1,999 list price; 575W draw needs a high-wattage power supply; 70B models still do not fit at 4-bit.
- Street price: About $4,300–$5,000+ (October 2026).
Check the PNY RTX 5090 OC Triple Fan on Amazon (affiliate link)
2. Best value NVIDIA: RTX 4090 (used or refurbished) or RTX 3090
- Why: Ada Lovelace with 16,384 CUDA cores and full CUDA compatibility; widely used with ComfyUI and llama.cpp.
- Pros: Mature ecosystem; 24GB handles models up to about 32B at 4-bit; far cheaper than a new RTX 5090.
- Cons: Previous generation, so supply is mostly used or refurbished; used cards have warranty and wear risk; 450W draw.
- Street price: About $2,150–$2,350 used (Sept 2026). A used RTX 3090 (24GB, about $1,250) is the budget alternative.
See RTX 4090 listings on Amazon (affiliate link; confirm the seller and condition)
3. Best professional NVIDIA: H100 (rent), with H200 and B200 as upgrades
- Why: The mature, widely available data-center workhorse with the deepest software support; supports MIG partitioning.
- Pros: Scales to large training clusters; abundant rental supply at falling rates.
- Cons: Expensive to buy (about $25,000–$40,000 per card); most users should rent.
- Rental: Median about $3.50 per GPU-hour on-demand, with listings from about $1.30 to $12.29. B200 rentals run about $3.75–$6.39 at specialist clouds.
4. Best AMD for data centers: Instinct MI355X (MI300X for rentals)
- Why: Memory capacity. MI355X carries 288GB HBM3E and FP4/FP6 support; MI300X carries 192GB HBM3 and is the easier chip to rent today.
- Pros: Large memory fits big models on fewer GPUs; AMD reports competitive MLPerf results against B200 on selected tasks.
- Cons: ROCm setup can take more effort; MI355X cloud listings are still limited; MI355X power is rated up to about 1,400W.
- Rental: MI300X about $3.59–$4.98 per GPU-hour; MI355X listings are scarce.
5. Best value AMD: Radeon RX 9070 XT (16GB) or Radeon AI PRO R9700 (32GB)
- Why: Both use the RDNA 4 “Navi 48” chip. The R9700 doubles the memory to 32GB; the RX 9070 XT has 16GB (not 24GB as an earlier version of this article stated).
- Pros: Lower cost per GB of VRAM than the RTX 5090; the R9700 is a 300W, two-slot design built for multi-GPU local AI.
- Cons: Fewer AI tutorials and some CUDA-only tools; the 16GB RX 9070 XT limits model size; memory bandwidth is lower than RTX 3090/4090.
- Street price: RX 9070 XT about $760–$830 ($599 list); R9700 about $1,400–$1,900 ($1,299 list).
Search RX 9070 XT on Amazon · Search Radeon AI PRO R9700 on Amazon (affiliate links)
6. Next generation to watch: AMD Instinct MI455X (Helios) vs NVIDIA Vera Rubin
- Why: Both vendors are moving to rack-scale systems and HBM4 in late 2026. AMD states up to 432GB HBM4 per MI455X GPU; NVIDIA says Vera Rubin cuts inference cost per token for mixture-of-experts models by up to 10x versus Blackwell (vendor claim).
- Who it is for: Cloud providers and large enterprises, not individual buyers.
- What to watch: Real shipment volumes, independent benchmarks and ROCm maturity at scale.
When to choose NVIDIA vs AMD
- Choose NVIDIA if you want the fewest compatibility surprises, run CUDA-only tools, need proven multi-GPU scaling, or are buying for a team that cannot afford debugging time.
- Choose AMD if you need more VRAM per dollar, are comfortable with Linux and ROCm, run supported open-source frameworks, or are renting memory-heavy inference capacity.
- Hybrid approach (a practical heuristic, not a benchmarked rule): train or prototype on NVIDIA, then test AMD for cost-sensitive inference once your stack runs on both.
- For most individuals in late 2026: rent data-center GPUs for big jobs, and buy a used 24GB NVIDIA card or a 32GB Radeon AI PRO R9700 for local work, rather than paying 2–2.5x list price for a new RTX 5090.
AI personalization guide: use this article with Claude, ChatGPT, Gemini and Perplexity
You can paste your budget, operating system and target models into an AI assistant and ask it to apply this article's tables. Because prices change daily, ask the assistant to flag figures you should verify.
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| Assistant | How to use this article | Example prompt |
|---|---|---|
| Claude | Paste the article text or link and your constraints; ask for a ranked shortlist with assumptions and risks | “I have a budget of [amount], run [Windows/Linux], and want to run [model name and size] locally. Using the VRAM guide and price ranges in the article at onedayadvisor.com/2025/10/Nvidia-vs-AMD-GPUs-AI.html, rank three GPUs and tell me which assumptions to verify before buying.” |
| ChatGPT | Use with web search turned on so current street prices can be checked | “I have a budget of [amount], run [Windows/Linux], and want to run [model name and size] locally. Using the VRAM guide and price ranges in the article at onedayadvisor.com/2025/10/Nvidia-vs-AMD-GPUs-AI.html, rank three GPUs and tell me which assumptions to verify before buying. Check current street prices and cite your sources.” |
| Gemini | Ask it to compare listings and confirm specs against vendor pages | “I have a budget of [amount], run [Windows/Linux], and want to run [model name and size] locally. Using the VRAM guide and price ranges in the article at onedayadvisor.com/2025/10/Nvidia-vs-AMD-GPUs-AI.html, rank three GPUs and tell me which assumptions to verify before buying. Verify the VRAM and power specs against the vendor pages.” |
| Perplexity | Ask for a source-cited comparison and recent news on availability | “I have a budget of [amount], run [Windows/Linux], and want to run [model name and size] locally. Using the VRAM guide and price ranges in the article at onedayadvisor.com/2025/10/Nvidia-vs-AMD-GPUs-AI.html, rank three GPUs and tell me which assumptions to verify before buying. Cite sources and note anything that changed in the last 30 days.” |
Frequently asked questions
Is NVIDIA or AMD better for AI GPUs in 2026?
NVIDIA is the safer default in 2026 because CUDA is the most widely supported AI software stack and its GPUs work with most tools out of the box. AMD is the better value in some cases: the Radeon AI PRO R9700 offers 32GB of VRAM for far less than an RTX 5090, and AMD says its Instinct MI355X is competitive with NVIDIA's B200 in some MLPerf tests.
How much VRAM do I need to run local AI models?
As a rough guide, a 4-bit quantized model needs about 0.5 to 0.6 GB of VRAM per billion parameters, plus room for context. That puts 8B models near 5GB, 32B models near 19GB, and 70B models near 40GB. A 24GB card suits models up to about 32B, while 70B models need two GPUs, a larger card, or CPU offloading.
Can AMD GPUs run PyTorch, Stable Diffusion, and local LLMs?
In many cases, yes. AMD's ROCm software supports PyTorch, and tools such as llama.cpp can run on Radeon GPUs, with Linux generally the best-supported route. Compatibility depends on your exact GPU and operating system, and some ComfyUI custom nodes assume CUDA, so check AMD's ROCm compatibility list and your tool's documentation before buying.
Why is the RTX 5090 so expensive in 2026?
Street prices of roughly $4,300 to $5,000 or more, versus a $1,999 list price, trace largely to a GDDR7 memory shortage as memory supply is directed toward AI data-center products, according to price-tracker and press reports. NVIDIA's official list prices have not changed; the markup appears in retail and resale prices. Prices move quickly, so check live listings.
Is the Radeon AI PRO R9700 worth buying for local AI?
It can be if you need 32GB of VRAM and run Linux with ROCm-friendly tools. It uses the same chip as the RX 9070 XT with doubled memory, carries a $1,299 list price, and has sold for about $1,400 to $1,900 in September 2026. Its memory bandwidth is lower than an RTX 3090 or 4090, so weigh software fit first.
How much does it cost to rent an H100 or MI300X?
On October 10, 2026, one tracker listed a median on-demand H100 price of $3.50 per GPU-hour, with listings from about $1.30 to $12.29 depending on provider. A September tracker averaged about $4.98 per hour for AMD MI300X across six providers. Hyperscalers usually cost more than specialist GPU clouds, and rates change often, so compare current prices.
Will AMD's MI450 and Helios overtake NVIDIA's Vera Rubin?
It is too early to say. AMD says Helios, built around the MI455X, begins shipping in the third quarter of 2026 and ramps into 2027, with commitments from OpenAI, Oracle, Microsoft and others. NVIDIA still holds an estimated 75% to 85% of AI accelerator revenue, and CUDA remains its main advantage. Independent head-to-head results for these new systems are still limited.
Does the NVIDIA vs AMD GPU race tell me which stock to buy?
Not by itself. GPU performance, market share, margins, customer concentration and valuation all affect a stock, and analyst share estimates for 2026 vary widely, from roughly 75% to 85% for NVIDIA and about 5% to 7% for AMD. This article is educational, not investment advice. See our AI stock and ETF guides for investment-focused analysis.
What changed in this update (October 11, 2026)
This update corrects several specification errors and replaces unsourced benchmark numbers with sourced results. Original URL kept for continuity.
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| Item | Earlier version | Now | Reason |
|---|---|---|---|
| RX 9070 XT memory | 24GB GDDR6 | 16GB GDDR6 | Specification correction |
| RTX 5090 specs | 10,240 CUDA cores; 600W TDP | 21,760 CUDA cores; 575W | Specification correction |
| RTX 5090 price | $2,000–$3,000 | About $4,300–$5,000+ street ($1,999 list) | 2026 memory-shortage pricing |
| MI300X memory | 192GB HBM3e | 192GB HBM3 | Specification correction |
| MI350X | “Upcoming 2025 release” | MI350/MI355X launched June 2025; MI400 series launched July 2026 | Roadmap update |
| Benchmark table | Unsourced tokens/s and images/s figures, including single-GPU 70B results | MLPerf-based results and a VRAM-fit guide | Figures could not be sourced; 70B does not fit on 24–32GB at 4-bit |
| Market claims | “CUDA powers over 90% of AI frameworks”; “AMD could hit 20–30% share by 2028” | Analyst share estimates with ranges | Original claims were unsourced |
| RTX 5090 “multi-frame generation up to 4x faster AI rendering” | Listed as an AI benefit | Removed | A gaming feature, not an AI-workload metric |
| Headings and dates | Mixed 2025 and 2026 | 2026 throughout; visible update date added | Freshness |
Sources and related reading
Primary and vendor sources:
- AMD: MLPerf Training 6.0 results (vendor-stated)
- AMD: MLPerf Inference 6.0 results (vendor-stated)
- MLCommons: MLPerf Training benchmarks
- TechPowerUp: Radeon AI PRO R9700 launch and $1,299 price
- AWS: EC2 P5 instances
Market, price and press sources (snapshots; verify before buying):
- Capital & Compute: RTX 50 MSRP vs street
- BestValueGPU: RTX 5090 price history
- BestValueGPU: RX 9070 XT price history
- RunAIHome: Radeon AI PRO R9700 local AI guide (Sept 2026)
- GetDeploying: H100 rental prices (Oct 10, 2026)
- Mercatus GPU Index: rental prices (Sept 13, 2026)
- Silicon Analysts: AI accelerator market share
- Reuters via Yahoo Finance: AMD Helios and MI450 launch
- Fierce Network: AMD Advancing AI 2026
- The Decoder: MLPerf Inference v6.0
- Tech Insider: RTX 5090 pricing and reported RTX 60 delay
Related reading on One Day Advisor:
- Best Selling GPUs for AI on Amazon
- 5 Best GPUs for AI Video Generation
- Best Low-VRAM Local Video Models: Render Times vs. VRAM Thresholds
- AI Infrastructure Stocks & ETFs: The Complete Investor Guide 2026–2030
- Best AI Stocks & ETFs for 2026
This article is for educational purposes only and is not investment, purchasing or professional advice. Specifications and prices change; verify with the manufacturer and current listings before you buy.
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