RTX 4090 vs RTX 3090 for Local LLMs — Which Should You Buy in 2026?

Both cards pack 24GB of VRAM. Both run the same models. The RTX 4090 costs nearly twice as much. Here's whether that gap is worth it for running Llama, DeepSeek, and friends at home.

At a Glance

• Same model capacity: Both have 24GB VRAM — you run identical model sizes on either card
• RTX 4090 is ~30% faster: 128 tok/s vs 112 tok/s on 8B models; 52 vs 42 tok/s on 70B Q4 (bestgpusforai.com, 2026)
• RTX 4090 price: ~$2,755 new on Amazon — 71% above its original $1,599 MSRP
• RTX 3090 price: $712–$1,000 used, ~$1,488 new (bestvaluegpu.com, 2026)
• Power gap: 450W (4090) vs 350W (3090) — that extra 100W adds up over time
• Verdict: RTX 3090 used wins on value for most home users; RTX 4090 is for speed-first workflows with budget to spare

Specs Comparison

The 4090's architectural lead is substantial on paper: 56% more CUDA cores, 12x the L2 cache, and 2.3x the Tensor throughput. In practice, LLM inference doesn't saturate all of that — but the gap does show up in real benchmarks.

Performance: Tokens Per Second

Raw inference speed is where the RTX 4090 earns its premium. In LLM benchmarks (hardware-corner.net, 2026):

• 8B models (Llama 3, Mistral): RTX 4090 — 128 tok/s | RTX 3090 — 112 tok/s
• Llama 3.1 70B Q4: RTX 4090 — 52 tok/s | RTX 3090 — 42 tok/s
• Median improvement across 8 benchmarks: 4090 leads by 16–40% depending on model and quantization

At 52 tok/s on 70B Q4, the 4090 feels like a fast local chat session. At 42 tok/s, the 3090 is still perfectly usable — responses appear faster than you read them. The real-world feel difference is meaningful for high-throughput batch jobs, but barely noticeable for single-user chat.

Where the 4090 pulls ahead more noticeably: long-context workloads and FP8 inference. The Ada Lovelace architecture supports FP8 natively; the 3090 doesn't. If you're running TensorRT-LLM or targeting inference optimization stacks that exploit FP8, the 4090's advantage widens.

VRAM: What Can You Actually Run on 24GB?

Since both cards share the same 24GB ceiling, model compatibility is identical. Here's what fits:

The 24GB sweet spot lands on everything up to 32–34B at Q4, plus quantized 70B models. Neither card gives you a capacity edge here — if a model runs on one, it runs on the other. VRAM is the constraint; compute speed is not.

Price & Value: The Decisive Factor

This is where the comparison gets stark.

The RTX 4090 currently trades at $2,755 new on Amazon — 71% above its launch price, with supply constraints expected to push prices another 10–20% higher through mid-2026 (gpudeals.net, 2026). There's no meaningful used market for 4090s.

The RTX 3090 tells a different story:

• New: ~$1,488 (essentially at original MSRP)
• Used: $712–$1,000 — validated by multiple marketplaces

A used 3090 at $800 vs a new 4090 at $2,755 means you're paying 3.4× more for ~30% more speed. In raw terms, the 4090 delivers roughly 0.9% more performance per dollar spent. The 3090 used wins this calculation by a wide margin.

The one exception: if you're running a multi-GPU inference server, the 4090's better memory bandwidth and FP8 support change the calculus. For a single-card home build, the math doesn't support the premium.

Power Draw: The Hidden Monthly Cost

Both cards are power-hungry, but the gap matters at the budget level.

An extra $44/year sounds minor. Over three years of daily use, that's $130 — which buys a decent NVMe drive or helps cover a PSU upgrade. If you're in a region with higher electricity rates (say $0.30/kWh), that gap doubles to ~$88/year.

You also need a beefier PSU for the 4090. NVIDIA recommends 850W minimum; most builders target 1000W for headroom. If you're upgrading from a 750W unit, factor in that cost too.

Which Should You Buy?

Use this decision matrix:

Buy the RTX 3090 (used) if:

• Budget is under $1,500
• You primarily run chat/inference (not batch processing)
• You want the best dollar-per-performance ratio
• You're fine running models up to ~32B Q4 with excellent speed

Buy the RTX 4090 if:

• Speed is your top priority and budget isn't the constraint
• You're running FP8 inference or TensorRT-LLM workloads
• You plan to run high-throughput batch jobs (multiple requests, agentic pipelines)
• You want a card that won't need replacing for 5+ years

Neither card makes sense if:

• You need more than 24GB VRAM (look at RTX 5090 at 32GB, or dual-GPU setups)
• You're on a tight budget (RTX 3060 12GB at ~$300 runs 7–13B models fine)

FAQ

Can the RTX 3090 still keep up in 2026? Yes, for most home users. Its 24GB VRAM and ~112 tok/s on 8B models make it more than capable for chat, coding assistants, and local inference. The architecture is older, but LLM inference workloads don't require bleeding-edge compute.

Is the RTX 3090 reliable to buy used? Generally yes. Most used 3090s on the market came from gaming or crypto mining rigs, not heavy AI workloads. Check for temperature records if the seller provides them, and buy from reputable resellers with return policies. The card's longevity track record is strong.

Does the RTX 4090 support any models the 3090 can't run? No — both max out at 24GB VRAM, so model compatibility is identical. The 4090 runs those models faster, not larger.

What about the RTX 5090? The RTX 5090 offers 32GB VRAM, which unlocks unquantized 70B models and larger 32B variants. At ~$2,000+ current pricing, it's a strong alternative to the 4090 for future-proofing. See our GPU deals page for current pricing.

Which should I buy for DeepSeek R1? Both run DeepSeek R1 32B Q4 comfortably within 24GB. The 4090 generates responses faster, but the 3090's output is still ahead of real-time reading speed. For DeepSeek R1 70B, you'll need quantization (Q2–Q3) on either card.

Bottom Line

The RTX 3090 and RTX 4090 are the only consumer GPUs with 24GB VRAM outside of workstation cards — and that shared ceiling means they run the same models. The difference is speed and price.

At current market rates, the 3090 used offers 3–4× better value per dollar for local LLM inference. The 4090 is faster — ~30% across most workloads — but that premium is priced at 3.4× the cost of a quality used 3090.

For most home builders, the used RTX 3090 is the call. If you're building a high-throughput inference server or just want the fastest single-GPU setup available at 24GB, the 4090 is the clear winner — assuming you can swallow the markup.

Not sure which GPU fits your specific setup? Take our GPU selector quiz or check the latest GPU deals for current pricing.

Affiliate Links

• RTX 4090 on Amazon
• RTX 3090 on Amazon

Sources

1. RTX 3090 vs RTX 4090 for AI — Performance & Upgrade Analysis (bestgpusforai.com, 2026)
2. RTX 4090 LLM Benchmarks: 4K–131K Context (hardware-corner.net, 2026)
3. RTX 4090 Price History & Specs (bestvaluegpu.com, 2026)
4. RTX 3090 Price History & Specs (bestvaluegpu.com, 2026)
5. RTX 4090 Deals (gpudeals.net, 2026)
6. Used RTX 3090 — Value King for Local AI (xda-developers.com, 2026)