NVIDIA GeForce RTX 3090 Ti

Highest-tier Ampere consumer card. Used market gold for AI: 24GB at sub-$1200 in 2026.
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Sub-scores sum to 743 / 1000. Headline = 743 × 0.70 (Estimated-confidence discount) = 520. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
Extrapolated from 1008 GB/s bandwidth — 121.0 tok/s estimated. No measured benchmarks yet.
Plain-English: Workable at 32B, comfortable at 14B and below — snappy enough for a coding agent; vision models supported.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
What it does well
The RTX 3090 Ti is the late-Ampere flagship — a refined 3090 with bumped clocks, GDDR6X memory at 1008 GB/s (vs 3090's 936 GB/s), and a slightly more aggressive thermal envelope. 24 GB GDDR6X at 1.0 TB/s + Ampere tensor cores at $1,999 MSRP / $700–$1,100 used. For everything that fits 24 GB, it's marginally faster than RTX 3090 on memory-bound decode (the difference is ~5–8% in real LLM workloads — the bandwidth bump matters but not transformationally). Power draw at 450 W TDP is brutal — same as RTX 4090 and substantially more than 3090's 350 W. The card was the "halo SKU" of Ampere — released near the end of the architecture's commercial life — so it's relatively rare in used markets, but available with strong service histories from gamers who upgraded. Full CUDA stack works (sm_86 Ampere): Ollama, LM Studio, llama.cpp, vLLM, ExLlamaV2. For buyers who specifically value the marginal bandwidth advantage over 3090 and accept the power+heat tradeoff, RTX 3090 Ti is the niche flagship-Ampere pick.
Where it breaks
- Marginal vs RTX 3090 — pricing usually doesn't justify the gap. Used 3090 at $700-1000 vs used 3090 Ti at $700-1100 = nearly identical pricing for ~7% more bandwidth and 28% more power draw. For most buyers, regular 3090 wins on $/throughput and TCO.
- 450 W TDP is a real planning problem. Sustained inference at 450 W needs serious case airflow + a quality 1000 W+ PSU + acceptance of meaningful summer heat in the room. The 3090's 350 W is much more practical.
- No FP8 native (Ampere limitation). Modern frameworks that exploit FP8 throughput don't get speedup. Same constraint as all Ampere.
- Architecture is two generations behind in 2026. Ada Lovelace (RTX 4090) and Blackwell (RTX 5090) deliver dramatically better tensor compute. New CUDA features land on Ada / Blackwell first.
- Resale liquidity is awkward. RTX 3090 has very high secondary-market volume; 3090 Ti's smaller production run means less price discovery. Resale pricing tends to wobble with availability.
- Pricing unclear vs RTX 4090. Used 4090 at $1,500–$1,800 has FP8 native + ~70% more compute + better thermals + same 24 GB. The 3090 Ti's natural niche is squeezed from both sides — by 3090 below and 4090 above.
Ideal model range
- Sweet spot: 70B Q4 single-card with 16K context — fits 24 GB comfortably. 25–35 tok/s decode (slightly faster than regular 3090).
- Sweet spot: 32B FP16 with 32K context, 32B Q8 with 128K+ context for long-document workflows.
- Sweet spot: Multi-model agentic stacks fitting 24 GB — 14B + 7B + embedding model simultaneously.
- Sweet spot: Local fine-tuning at 13B QLoRA, 7B FP16 full fine-tune.
- Comfortable: Anything an RTX 3090 does, with marginal bandwidth advantage.
Bad use cases
- Buyers shopping new at MSRP. $1,999 retail in 2026 is wildly overpriced. Pick used 3090 ($700-1000) or used 4090 ($1,500–$1,800) instead.
- Cost-conscious 24 GB seekers. Used 3090 at $700–$1,000 is dramatically better $/$ for almost identical AI throughput.
- Power-constrained desktops. 450 W TDP is too much for many builds. Pick 3090 (350 W) or 4090 (450 W but with better perf/W).
- Anyone wanting current-gen architecture features. Pick RTX 4090 (Ada FP8) or RTX 5090 (Blackwell FP4).
- 70B+ workloads. Same as all 24 GB cards — pick 32 GB+ for 70B FP16, 48 GB+ for serious 70B-class production.
Verdict
Buy this if you find a 3090 Ti at $700–$900 used (similar to 3090 pricing), you specifically value the ~7% bandwidth advantage on memory-bound decode, you have power+thermal headroom for 450 W TDP, and a regular 3090 isn't available in your local used market. RTX 3090 Ti is the niche pick for late-Ampere collectors and buyers who want flagship-Ampere positioning.
Skip this if used RTX 3090 is available at similar pricing (almost always wins on $/$), you can stretch to used RTX 4090 (~$1,500–$1,800 with Ada-gen + FP8), you're power-constrained (3090 at 350 W is much more practical), or you're shopping new (MSRP at $1,999 is unreasonable in 2026).
How it compares
- vs RTX 3090 (24 GB) → Same memory tier, same architecture. 3090 Ti has ~7% more bandwidth (1.0 TB/s vs 936 GB/s) + ~10% more compute + 28% more power draw at similar used pricing. Pick regular 3090 for $/$ ; 3090 Ti only when 3090 is unavailable or specifically priced lower. See /compare/rtx-3090-ti-vs-rtx-3090.
- vs RTX 4090 (24 GB) → Same 24 GB. 4090 has Ada-gen + FP8 + ~70% more compute + same 450 W TDP at $1,500–$1,800 used vs 3090 Ti $700–$1,100. Pick 4090 for FP8 + Ada-gen on 24 GB; 3090 Ti for value at ~half the price.
- vs RTX 5090 (32 GB) → 5090 has 33% more VRAM + ~80% more bandwidth + Blackwell + FP4 native at $2,000–$2,500. Pick 5090 for new builds; 3090 Ti for value used.
- vs RTX A6000 (Ampere) (48 GB) → Same Ampere architecture, A6000 has 2× memory + ECC + Studio drivers + workstation pedigree at $3,500–$4,500 used. Pick A6000 for 48 GB workstation; 3090 Ti for cost-floor 24 GB with similar Ampere generation.
Overview
Highest-tier Ampere consumer card. Used market gold for AI: 24GB at sub-$1200 in 2026.
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Specs
| VRAM | 24 GB |
| Power draw (peak) | 450 W |
| Released | 2022 |
| MSRP | $1999 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce RTX 3090 Ti with usable context.
Frequently asked
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Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.