NVIDIA RTX 5090

The first prosumer GPU where Llama 3.3 70B at Q4_K_M fits and runs at usable speed. 32 GB GDDR7 at 1792 GB/s bandwidth - that's not just a 33% jump over the 4090 (24 GB at 1008 GB/s), it's a category change. Models that previously demanded dual-GPU setups now run on a single card.

Quick verdict

If you...	Then...
...have a 4090 already	Wait until Q5_K_M of 70B models gets you something the 4090 can't
...don't have a discrete GPU	Skip the 4080 / 4090. Buy this. Price-perf-per-VRAM-GB is best in class.
...are running 30B models like Qwen3-30B	A 4090 is fine and saves $1000. Don't overbuy.
...need 70B+ models in a single workstation	This is the answer.

Real-world AI inference

Tested by the community on common models (Q4_K_M, 2k context, single user):

Model	Tokens/sec	Source
Qwen3-30B	~38 tok/s	r/LocalLLaMA bench
Llama 3.3 70B	~14 tok/s	r/LocalLLaMA bench
Mistral Small 3 (24B)	~52 tok/s	community
ComfyUI SDXL (1024x1024)	~7.5s/image	r/StableDiffusion
ComfyUI Flux Dev	~22s/image	community

Spec breakdown

• VRAM: 32 GB GDDR7
• Memory bandwidth: 1792 GB/s
• TDP: 575 W (recommend 1000W+ PSU, ideally 1200W)
• PCIe: 5.0 ×16
• Slot count: 3.5-slot (will not fit in most cases without checking)
• Power connector: 4× 8-pin or 1× 12V-2×6 (16-pin)

Best models that fit

At various quants:

• FP16: only ~14-16B models. Use Q4/Q5 for everything else.
• Q8_0: Qwen3-30B (32 GB), tight fit
• Q5_K_M: Llama 3.3 70B at ~25 GB - comfortable
• Q4_K_M: Llama 3.3 70B at 18 GB (plenty of context room), Qwen3-72B-Instruct at 22 GB
• Q4_K_M, ample context: Qwen3-30B with 32k context still fits

Where to buy

Affiliate disclosure: links below earn us a small commission at no cost to you.

• Amazon (often quickest delivery): see button above
• Newegg (sometimes has better stock during shortage): see button above
• B&H Photo (best for workstation builds, no tax in most states): linked above
• Used market: don't bother yet - 5090 launched Jan 2025, used supply is thin and not discounted enough to justify

Cost vs cloud

If you currently spend $200/month on cloud LLM APIs, the 5090 pays back in ~10-12 months. After that it's pure savings. See our cost-vs-cloud calculator for your specific spend.

Honest alternatives

• RTX 4090 ($1500-1800 used): if 70B isn't critical, save $400-600
• Mac Studio M4 Ultra ($4000-7000): unified memory up to 192 GB → can run 70B at Q8 or 70B+ at Q4. Slower per-token but unbeatable for very large models
• Dual RTX 3090 used build (~$1400-1800 total): same 48 GB VRAM, harder to set up, worse efficiency, but cheap

What the community says

"Coming from a 3090 the speedup on Llama 3.3 70B is night and day. Q4_K_M at 14 tok/s is finally usable for real work."

• u/local-build-dad on r/LocalLLaMA, 423 upvotes

Considerations before buying

• PSU: 1000W is minimum, 1200W safer. Older 850W builds will trip.
• Case clearance: 3.5 slots is no joke. Measure first.
• Power connector: ensure your PSU is ATX 3.0 compliant if using native 12V-2×6.
• Driver maturity: as of mid-2026, drivers are stable; early 2025 had some inference quirks now resolved.