NVIDIA · Blackwell
Best AI Models for NVIDIA GeForce RTX 5090 (32 GB)
Runs 27–31B and 35B MoE models well; 70B doesn't fit in 32 GB.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 3 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 213 t/s | 213 tok/s | Q4_K_M · 22.0 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 3 | Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed | Math Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 213 t/s | 213 tok/s | Q4_K_M · 22.0 GB | |
| 4 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | overall | Q4_K_M · 21.7 GB |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 357 t/s | 357 tok/s | Q4_K_M · 3.4 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 282 t/s | 282 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 4 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB | |
| 5 | Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 227 t/s | 227 tok/s | Q4_K_M · 19.4 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | JEV 27B VLautotrust Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB |
The RTX 5090 is the quickest card in the 24–32 GB class: it runs 27–31B and 35B MoE models well. It is the most efficient per watt in its tier, and pricier per token than most of the cards compared at ≈$5,376. Blackwell generation, released 2025.
RTX 5090 strengths and weaknesses
Strengths
- Fastest card here: 78% faster than the RTX 4090, 183 vs 103 tok/s on Qwen3.5 9B
- Runs 35B MoE models: Qwen3.6 35B A3B at 213 tok/s in 22 GB
- Room for long context: Qwen3.8 27B at Q4 uses 17.9 GB, leaving 14.1 GB free
- Reads long prompts fast: A 4K-token prompt on Qwen3.8 27B starts answering in 2.4 s
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 32 GB here
- 575 W under load: Plan a 1,150 W PSU; ≈$126 a year at 4 h a day
- Priced well above MSRP: ≈$5,376 new vs its $1,999 MSRP (+169%)
Speed per dollar and per watt
On Qwen3.5 9B (9B params) at Q4_K_M. New prices are compared with new, used with used. The last two columns are the speed you get per $100 of price and per 100 W of board power; higher is better.
| Card | New price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RX 7900 XTX24 GB | about $1,130 | 90.6 | 8.0 | 25.5 |
RTX 5070 Ti16 GBBest new value | about $1,170 | 91.6 | 7.8 | 30.5 |
RX 907016 GB | about $800 | 60.4 | 7.6 | 27.5 |
RX 9070 XT16 GB | about $815 | 60.4 | 7.4 | 19.9 |
RTX 508016 GB | about $1,690 | 98.1 | 5.8 | 27.3 |
RX 9060 XT 16GB16 GB | about $590 | 30.2 | 5.1 | 18.9 |
RTX 509032 GB | about $5,376 | 183 | 3.4 | 31.8 |
| Card | Used price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RX 907016 GBBest used value | about $650 | 60.4 | 9.3 | 27.5 |
RX 7900 XTX24 GB | about $1,000 | 90.6 | 9.1 | 25.5 |
RTX 5070 Ti16 GB | about $1,100 | 91.6 | 8.3 | 30.5 |
RTX 3090 Ti24 GB | about $1,600 | 103 | 6.4 | 22.9 |
RTX 309024 GB | about $1,500 | 95.7 | 6.4 | 27.3 |
RTX 508016 GB | about $1,640 | 98.1 | 6.0 | 27.3 |
RTX 409024 GB | about $3,000 | 103 | 3.4 | 22.9 |
RTX 509032 GB | about $6,400 | 183 | 2.9 | 31.8 |
Cost per token: the RTX 5090 against 16–32 GB cards
On Qwen3.5 9B (9B params, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.
- Card, new price
- $1.86
- Power
- $0.13
Renting costs as much as buying after ≈10,800 hours (≈7.4 years at 4 h a day).
Each dot is a card; hover or tap it for its numbers, click to open it. Cards without a price for the chosen basis are left out. How we calculate cost per token
Cards people weigh against the RTX 5090, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (1792 GB/s). Grey rows need more than 32 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB183tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB123tok/s
- 27–31BQwen3.8 27B · Q4_K_M · 17.9 GB66.9tok/s
- 35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB213tok/s
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 1176 models on the RTX 5090
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
Qwen3.5 9BVisionFunctions | Q4_K_M | 6.6 GB | 183.1 t/s | 262K | S99, Runs great | |
Hy Memory 30B A3BChatFunctions | Q4_K_M | 19.1 GB | 237.0 t/s | 262K | S100, Runs great | |
Laguna XS 2.1ChatFunctions | Q4_K_M | 21.3 GB | 264.1 t/s | 262K | S100, Runs great | |
Ornith 1.0 35BChatFunctions | Q4_K_M | 21.5 GB | 235.5 t/s | 262K | S100, Runs great | |
Nanbeige4.3 33BChatFunctions | Q4_K_M | 23.7 GB | 56.5 t/s | 524K | A82, Runs well | |
Qwen3.6 27BVisionFunctions | Q4_K_M | 17.9 GB | 66.9 t/s | 262K | S85, Runs great | |
Ling 3.0 TinyChatFunctions | Q4_K_M | 5.5 GB | 471.1 t/s | 131K | S100, Runs great | |
Laguna XS.2ChatFunctions | Q4_K_M | 21.3 GB | 264.1 t/s | 262K | S100, Runs great | |
Qwen3.5 4BVisionFunctions | Q4_K_M | 3.4 GB | 357.3 t/s | 262K | S100, Runs great | |
Underdog Saluki 27B 1.0ChatFunctions | Q4_K_M | 21.4 GB | 65.4 t/s | — | S85, Runs great | |
MiniCPM5 2BChatFunctions | Q4_K_M | 2.5 GB | 613.1 t/s | 131K | S100, Runs great | |
Gemma 4 12B ITChatFunctions | Q4_K_M | 9.0 GB | 141.5 t/s | 262K | S96, Runs great | |
Spark X2.6 4BChatFunctions | Q4_K_M | 4.3 GB | 393.5 t/s | 1049K | S100, Runs great | |
Qwen3.6 35B A3B Abliterated V4ChatFunctions | Q4_K_M | 21.3 GB | 249.2 t/s | 262K | S100, Runs great | |
| Q4_K_M | 17.9 GB | 66.9 t/s | 262K | S85, Runs great |
Specs
- Architecture
- Blackwell
- VRAM
- 32 GB GDDR7
- Memory bandwidth
- 1792.0 GB/s
- FP16 (dense)
- 209.50 TFLOPS
- CUDA cores
- 21,760
- Tensor cores
- 680
- Compute capability
- 10.0
- TDP
- 575 W
- Released
- 2025-01-30
- MSRP
- $1,999
RTX 5090 FAQ
Is the RTX 5090 still worth it in 2026?
At ≈$5,376 you get 3.4 tok/s per $100 on Qwen3.5 9B, which ranks 7th of the 7 cards compared here; it is also the fastest out of 6 cards with 24–32 GB. It runs 27–31B and 35B MoE models well; 70B doesn't fit in 32 GB.
What is the largest model the RTX 5090 can run?
Qwen3.6 35B A3B (36.0B) is the largest at 4-bit or better: at Q4_K_M it uses 22.0 GB of 32 GB, and runs at about 213 tok/s.
Can the RTX 5090 run Qwen3.8 27B?
Yes. Qwen3.8 27B fits at Q4_K_M, using 17.9 GB of 32 GB, and it is the best model that runs well on this card.
Can the RTX 5090 run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 32 GB on this card.
How many tokens per second does the RTX 5090 give?
About 66.9 tok/s on Qwen3.8 27B at Q4_K_M, with the first token after 2.4 s on a 4K-token prompt.
What power supply does the RTX 5090 need?
Plan a 1150 W PSU (2× the 575 W TDP).
Is 32 GB enough for coding and agents?
Yes for coding: Qwen3.8 27B, the best of the 10 coding models that fit, runs at about 67 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an RTX 5090?
Rent from $0.50/hr. Renting costs as much as buying after ≈10,800 hours (≈7.4 years at 4 h a day).