TL;DR: The RTX 5090 offers 33% more VRAM and 2.5x the AI TOPS, but you can't buy one. Street prices sit above $6,000, production is paused, and no new stock is expected until Q3 2026 at the earliest. Meanwhile, RTX 4090 cards are available new for $1,150-$2,600 and used for $1,800-$2,200. For most local AI workloads, the 4090's 24GB GDDR6X is still the sweet spot.
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The Math Doesn't Lie
On paper, the RTX 5090 wins every spec comparison against the 4090. In practice, you need to actually own the card for those specs to matter.
Here's where things stand in February 2026:
| RTX 4090 | RTX 5090 | |
|---|---|---|
| MSRP | $1,599 | $1,999 |
| Actual Street Price | $1,150-$2,600 (new) | $6,000+ |
| Availability | In stock at multiple retailers | Effectively zero US retail stock |
| VRAM | 24GB GDDR6X | 32GB GDDR7 |
| Memory Bandwidth | 1,008 GB/s | 1,792 GB/s |
| AI TOPS | ~1,321 | 3,352 |
| TDP | 450W | 575W |
| Production Status | Discontinued (existing stock) | Paused until Q3 2026+ |
The RTX 5090 is roughly 50-70% faster in AI inference benchmarks. But at 3-4x the actual purchase price, the performance-per-dollar calculation flips hard in the 4090's favor.
Here are five reasons to stop waiting.
Reason 1: 24GB Is Still Enough for Most Local AI
The VRAM anxiety around the 4090's 24GB is overblown for the majority of local AI use cases.
According to BestGPUsForAI benchmarks, here's what fits comfortably in 24GB:
- 7B parameter models (Q4 quantized): ~6GB VRAM — runs with room to spare
- 13B parameter models (Q4 quantized): ~10GB VRAM — comfortable fit
- 34B parameter models (Q4 quantized): ~20GB VRAM — tight but functional
- 70B parameter models (Q4 quantized): Requires offloading — this is where 32GB helps
For running Llama 3.1 8B, Mistral 7B, CodeLlama 34B, or Stable Diffusion XL, 24GB handles everything without breaking a sweat. The 5090's 32GB advantage only becomes meaningful at 70B+ parameter models or when running multiple models simultaneously.
Most local AI workflows — chatbots, code assistants, image generation, document processing — fit well within 24GB. The 4090 delivers roughly 4,200-4,800 tokens/sec on Llama 3.1 8B according to Bizon's benchmarks. That's fast enough for real-time inference in any practical application.
Reason 2: The Price Gap Is Absurd
Let's do the math on what you could buy for the price of one RTX 5090 at current street prices:
One RTX 5090 ($6,000+)
- 32GB GDDR7
- 3,352 AI TOPS
- 1,792 GB/s bandwidth
Two RTX 4090s (~$4,400 total at current new pricing)
- 48GB GDDR6X combined
- ~2,642 AI TOPS combined
- 2,016 GB/s bandwidth combined
- $1,600 left over
Two 4090s give you 50% more total VRAM, comparable aggregate bandwidth, and change left over for the rest of your build. Workstations like the Bizon G3000 support up to 4 GPUs, making multi-4090 configurations a legitimate strategy for serious AI work.
Even buying a single ASUS TUF RTX 4090 at $1,150 saves you nearly $5,000 versus the cheapest 5090 you might find. That's enough to fund an entire year of cloud GPU access for overflow workloads.
Reason 3: You Can Actually Buy One Today
This is the simplest argument and the most powerful one.
The RTX 5090 has had zero meaningful US retail availability since launch. Production is now fully paused. The optimistic timeline for restocking is Q3 2026 — six months from now — and even that assumes the DRAM memory crisis eases.
RTX 4090 cards, meanwhile, are available right now:
- New cards: Available from Amazon, retailers, and specialty shops at $1,150-$2,600 depending on model and availability
- Used cards: $1,800-$2,200 on eBay, with abundant supply from upgraders who bought 5090s early
- System integrators: Vendors like Bizon still build 4090-based workstations
Every month spent waiting for a 5090 is a month without local AI capability. If the goal is running models locally for development, privacy, or cost savings versus cloud APIs, the 4090 starts paying for itself immediately.
Reason 4: The Software Ecosystem Doesn't Care
CUDA doesn't run faster on newer cards because it's CUDA. The software stack — PyTorch, Ollama, LM Studio, ComfyUI, vLLM — works identically on both GPUs. There are no 5090-exclusive software features for AI workloads.
The RTX 5090 introduces FP4 precision support, which could theoretically enable more aggressive quantization. But as of February 2026, mainstream frameworks haven't broadly adopted FP4 inference paths. The practical software experience on a 4090 and 5090 is nearly identical for:
- Ollama/LM Studio: Same model compatibility, same quantization options
- Stable Diffusion / ComfyUI: Same workflows, same extensions
- PyTorch training: Same API, same CUDA toolkit
- vLLM / TGI inference: Same serving frameworks, same optimizations
The performance difference is real — the 5090 is genuinely faster — but you're running the same software either way. There's no "RTX 5090 required" label on any AI tool today.
Reason 5: The 4090 Holds Its Value
The RTX 4090 launched at $1,599 in October 2022. Over three years later, used cards sell for $1,800-$2,200 — above original MSRP. According to GPU price tracking data, the 4090 has appreciated rather than depreciated.
This isn't normal for GPUs, but the current market isn't normal either:
- Production ceased October 2024: No new 4090s are being manufactured, creating natural scarcity
- AI demand floor: The 4090's 24GB VRAM maintains a baseline demand from AI users that previous-gen gaming GPUs never had
- 5090 shortage halo effect: Every buyer priced out of a 5090 becomes a potential 4090 buyer
Buying a 4090 today isn't just a purchase — it's a reasonable store of value. If you use it for 12-18 months and resell when 5090 supply normalizes, the depreciation is likely to be minimal. Try saying that about almost any other piece of consumer electronics.
When You Should Wait for the 5090
To be fair, there are scenarios where the 5090's advantages justify waiting:
- 70B+ parameter models: If you regularly run unquantized or lightly quantized 70B models, 32GB makes a real difference
- Memory bandwidth-bound workloads: The 5090's 1,792 GB/s vs. 1,008 GB/s matters for large batch inference
- Future-proofing for 2027+ models: Model sizes will keep growing, and 32GB gives more runway
- You already have a functional GPU: If you're not GPU-less, waiting costs nothing
But for anyone sitting without local AI capability, waiting 6+ months for a card that will cost $2,000+ at best (and likely more) is a hard trade to justify.
The Bottom Line
| Factor | RTX 4090 | RTX 5090 |
|---|---|---|
| Can you buy it today? | Yes | No |
| Realistic price | $1,150-$2,600 | $6,000+ (if found) |
| 24GB enough for your workload? | Probably yes | N/A |
| Performance per dollar | Strong | Poor at current pricing |
| Resale value outlook | Stable/appreciating | Unknown |
| Time to first inference | This week | Q3 2026 at best |
The RTX 4090 isn't the best GPU for AI. The RTX 5090 is. But "best" only matters if you can actually buy one at a reasonable price. Right now, and for the foreseeable future, the 4090 is the smarter buy for the vast majority of local AI users.
Stop waiting. Start building.
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