RTX 3090 vs RTX 5090 for local LLMs
Assumes 32 GB of DDR5-5600 system RAM, Windows with this GPU driving the display, 8k context and an f16 KV cache.
What to know before choosing
The RTX 5090's extra 8GB pays off above Q4_K_M and at long context. Qwen3.6 27B at Q8_0, the higher-quality 8-bit file, needs 28.4 GB at 8k: it fits fully on the RTX 5090 and runs great, but that exceeds the RTX 3090's 23.4 GB usable, so part spills to system RAM and it runs slowly. With an f16 KV cache, Gemma 4 31B at 64k needs 24.8 GB: it stays fully on the RTX 5090 and runs great, while the RTX 3090 spills and runs it well.
DeepSeek R1 Distill Qwen 32B always thinks, so its speed bar is stricter: the RTX 3090 falls short and runs it well, while the RTX 5090 clears it easily and runs it great. Their estimate ranges do not overlap, so this is a real gap. Generation follows memory bandwidth, and in CanRun's estimate the gap is widest on dense models such as Gemma 4 31B, narrower on MoE models like Qwen3.6 35B-A3B. Prompt reading follows FP16 compute, where we estimate an even wider lead.
The GeForce RTX 5090 Laptop has only 24 GB and less bandwidth than either desktop card. In CanRun's estimate it matches the older desktop card on two models above: DeepSeek R1 Distill Qwen 32B runs well, and Qwen3.6 27B at Q8_0 spills to system RAM and runs slowly.
Speed
The RTX 5090 generates text about 1.9× faster than the RTX 3090 (median of our estimates over the 46 models that fit entirely in memory on both at Q4_K_M or the closest available quant).
On Qwen3.5 35B-A3B the two estimate ranges overlap, so the real gap may be smaller.
- Reference model
- Qwen3.5 35B-A3B at Q4_K_M, the largest model that fits entirely in memory on both at Q4_K_M or the closest available quant
- RTX 3090
- est. 148.8 tok/s (119.1–178.6, calibrated estimate ±20%)
- RTX 5090
- est. 213.4 tok/s (170.7–256.0, calibrated estimate ±20%)
- Prompt processing on Qwen3.5 35B-A3B
- RTX 3090: about 4,046 tok/s (2,427–5,664, rough estimate ±40%); RTX 5090: about 11,910 tok/s (7,146–16,673, rough estimate ±40%)
Specs side by side
| Spec | RTX 3090 | RTX 5090 | Difference |
|---|---|---|---|
| Memory | 24 GB | 32 GB | +8 GB on the RTX 5090 |
| Memory bandwidth | 936.0 GB/s | 1,792.0 GB/s | 1.91× on the RTX 5090 |
| FP16 compute | 35.6 TFLOPS | 104.8 TFLOPS | 2.94× on the RTX 5090 |
| Launch year | 2020 | 2025 | — |
| Price | $1,050 street (as of 2026-08-09) | $4,200 street (as of 2026-08-09) | — |
| Public benchmark results | 7 results | 15 results | — |
Verdicts at a glance
Out of 58 models at Q4_K_M (or the closest available quant) with 8k context.
- RTX 3090Runs great44 modelsRuns well4 modelsRuns slowly0 modelsWon't run10 models
- RTX 5090Runs great48 modelsRuns well1 modelRuns slowly1 modelWon't run8 models
How to read the verdicts
- Runs great
- Fully on the GPU at 20 tok/s or more
- Runs well
- Fully on the GPU at 8–20 tok/s; MoE experts in system RAM at 20 tok/s or more; or at least 90% on the GPU at 8 tok/s or more
- Runs slowly
- 2–8 tok/s; CPU-only; less than 90% on the GPU; or MoE experts in system RAM below 20 tok/s
- Won't run
- Does not fit, or under 2 tok/s
Where the verdicts differ
6 models get a different verdict on the two.
Scroll sideways to see every column.
| Model | RTX 3090 verdict | RTX 3090 speed | RTX 5090 verdict | RTX 5090 speed |
|---|---|---|---|---|
| K2-Horizon MoVA 36B-A4BQ4_K_M | Runs well | est. 79.2 tok/s55.5–103.0theoretical estimate ±30% | Runs great | est. 149.6 tok/s104.7–194.5theoretical estimate ±30% |
| DeepSeek R1 Distill Qwen 32BQ4_K_M | Runs well | est. 29.8 tok/s26.2–33.4calibrated estimate ±12% | Runs great | est. 57.0 tok/s50.2–63.9calibrated estimate ±12% |
| Qwen3 32BQ4_K_M | Runs well | est. 29.9 tok/s26.3–33.5calibrated estimate ±12% | Runs great | 53.3 tok/s8k estimate 57.3 tok/s (50.4–64.1, calibrated estimate ±12%)measured (1 run, 512 context) |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | Runs well | est. 122.0 tok/s85.4–158.7theoretical estimate ±30% | Runs great | est. 305.7 tok/s214.0–397.4theoretical estimate ±30% |
| Kolibri 1Q4_K_M | Won't runTry IQ4_XS: Runs well | est. 68.5 tok/s47.9–89.0theoretical estimate ±30% | Runs well | est. 83.3 tok/s58.3–108.3theoretical estimate ±30% |
| Llama 3.3 70BQ4_K_M | Won't runTry IQ4_XS: Runs slowly | est. 2.2 tok/s1.7–2.6calibrated estimate ±20% | Runs slowly | est. 2.7 tok/s2.2–3.3calibrated estimate ±20% |
Every model on both
Scroll sideways to see every column.
| Model | RTX 3090 verdict | RTX 3090 speed | RTX 5090 verdict | RTX 5090 speed |
|---|---|---|---|---|
| K2-Horizon MoVA 36B-A4BQ4_K_M | Runs well | est. 79.2 tok/s55.5–103.0theoretical estimate ±30% | Runs great | est. 149.6 tok/s104.7–194.5theoretical estimate ±30% |
| Qwen3.5 35B-A3BQ4_K_M | Runs great | est. 148.8 tok/s119.1–178.6calibrated estimate ±20% | Runs great | est. 213.4 tok/s170.7–256.0calibrated estimate ±20% |
| Qwen3.6 35B-A3BQ4_K_M | Runs great | est. 164.6 tok/s131.6–197.5calibrated estimate ±20% | Runs great | est. 229.8 tok/s183.9–275.8calibrated estimate ±20% |
| MiniCPM-V 4.7 35B-A3BQ4_K_M | Runs great | est. 174.2 tok/s139.3–209.0calibrated estimate ±20% | Runs great | est. 239.5 tok/s191.6–287.4calibrated estimate ±20% |
| EXAONE 4.5 33BQ4_K_M | Runs great | est. 31.8 tok/s28.0–35.6calibrated estimate ±12% | Runs great | est. 60.9 tok/s53.6–68.2calibrated estimate ±12% |
| DeepSeek R1 Distill Qwen 32BQ4_K_M | Runs well | est. 29.8 tok/s26.2–33.4calibrated estimate ±12% | Runs great | est. 57.0 tok/s50.2–63.9calibrated estimate ±12% |
| Qwen3 32BQ4_K_M | Runs well | est. 29.9 tok/s26.3–33.5calibrated estimate ±12% | Runs great | 53.3 tok/s8k estimate 57.3 tok/s (50.4–64.1, calibrated estimate ±12%)measured (1 run, 512 context) |
| EXAONE 4.0 32BQ4_K_M | Runs great | est. 33.0 tok/s29.0–36.9calibrated estimate ±12% | Runs great | est. 63.1 tok/s55.5–70.7calibrated estimate ±12% |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | Runs well | est. 122.0 tok/s85.4–158.7theoretical estimate ±30% | Runs great | est. 305.7 tok/s214.0–397.4theoretical estimate ±30% |
| Gemma 4 31BQ4_K_M | Runs great | est. 34.5 tok/s30.4–38.6calibrated estimate ±12% | Runs great | est. 66.1 tok/s58.1–74.0calibrated estimate ±12% |
| Qwen3 30B-A3B (2507)Q4_K_M | est. 140.6 tok/s112.5–168.7calibrated estimate ±20% | est. 195.0 tok/s156.0–234.1calibrated estimate ±20% | ||
| Muse Glimmer 30BQ4_K_M | Runs great | est. 37.6 tok/s33.1–42.1calibrated estimate ±12% | Runs great | est. 72.0 tok/s63.4–80.7calibrated estimate ±12% |
| Granite 4.2 30BQ4_K_M | Runs great | est. 33.0 tok/s29.0–36.9calibrated estimate ±12% | Runs great | est. 63.1 tok/s55.6–70.7calibrated estimate ±12% |
| Qwen3.5 27BQ4_K_M | Runs great | est. 37.1 tok/s32.7–41.6calibrated estimate ±12% | Runs great | est. 71.1 tok/s62.6–79.6calibrated estimate ±12% |
| Qwen3.6 27BQ4_K_M | Runs great | est. 37.7 tok/s33.2–42.3calibrated estimate ±12% | Runs great | est. 72.3 tok/s63.6–80.9calibrated estimate ±12% |
| Qwen3.8 27BQ4_K_M | Runs great | est. 36.4 tok/s32.1–40.8calibrated estimate ±12% | Runs great | 81.8 tok/s8k estimate 69.8 tok/s (61.4–78.2, calibrated estimate ±12%)measured (1 run, 1k context) |
| Gemma 3 27BQ4_K_M | Runs great | est. 38.0 tok/s33.5–42.6calibrated estimate ±12% | Runs great | est. 72.8 tok/s64.1–81.6calibrated estimate ±12% |
| Gemma 4 26B-A4BQ4_K_M | Runs great | 108.0 tok/s8k estimate 134.9 tok/s (108.0–161.9, calibrated estimate ±20%)measured (1 run, 32k context) | Runs great | 216.1 tok/s8k estimate 210.4 tok/s (168.3–252.4, calibrated estimate ±20%)measured (2 runs, 512/8k context) |
| Mistral Small 3.2 24BQ4_K_M | Runs great | est. 41.8 tok/s36.8–46.8calibrated estimate ±12% | Runs great | est. 80.0 tok/s70.4–89.6calibrated estimate ±12% |
| gpt-oss-20bMXFP4 | Runs greatgpt-oss-20b verdict on the RTX 3090 | 162.0 tok/s8k estimate 201.1 tok/s (160.9–241.3, calibrated estimate ±20%)measured (1 run, 2k context) | Runs greatgpt-oss-20b verdict on the RTX 5090 | 282.3 tok/s8k estimate 338.9 tok/s (271.1–406.7, calibrated estimate ±20%)measured (1 run, 2k context) |
| Kanana 1.5 15.7B-A3BQ4_K_M | Runs great | est. 156.3 tok/s125.0–187.5calibrated estimate ±20% | Runs great | est. 233.4 tok/s186.7–280.1calibrated estimate ±20% |
| Qwen3 14BQ4_K_M | Runs greatQwen3 14B verdict on the RTX 3090 | est. 63.4 tok/s55.7–71.0calibrated estimate ±12% | Runs greatQwen3 14B verdict on the RTX 5090 | est. 121.3 tok/s106.7–135.8calibrated estimate ±12% |
| DeepSeek R1 Distill Qwen 14BQ4_K_M | Runs great | est. 61.8 tok/s54.4–69.2calibrated estimate ±12% | Runs great | est. 118.3 tok/s104.1–132.5calibrated estimate ±12% |
| HyperCLOVA X SEED Think 14BQ4_K_M | Runs great | est. 64.3 tok/s45.0–83.5theoretical estimate ±30% | Runs great | est. 123.0 tok/s86.1–160.0theoretical estimate ±30% |
| Phi-4Q4_K_M | Runs great | est. 61.1 tok/s53.7–68.4calibrated estimate ±12% | Runs great | est. 116.9 tok/s102.9–131.0calibrated estimate ±12% |
| Gemma 3 12BQ4_K_M | Runs greatGemma 3 12B verdict on the RTX 3090 | est. 83.6 tok/s73.6–93.6calibrated estimate ±12% | Runs greatGemma 3 12B verdict on the RTX 5090 | est. 160.1 tok/s140.9–179.3calibrated estimate ±12% |
| Mellum2.1 12B-A2.5B ThinkingQ4_K_M | Runs great | est. 241.5 tok/s193.2–289.9calibrated estimate ±20% | Runs great | est. 334.9 tok/s267.9–401.8calibrated estimate ±20% |
| Gemma 4 12BQ4_K_M | Runs great | est. 90.3 tok/s79.5–101.2calibrated estimate ±12% | Runs great | est. 172.9 tok/s152.2–193.7calibrated estimate ±12% |
| Qwen3.5 9BQ4_K_M | Runs great | est. 106.7 tok/s93.9–119.5calibrated estimate ±12% | Runs great | est. 204.4 tok/s179.8–228.9calibrated estimate ±12% |
| K2-Horizon 7BQ4_K_M | Runs great | est. 96.4 tok/s84.8–107.9calibrated estimate ±12% | Runs great | est. 184.5 tok/s162.4–206.7calibrated estimate ±12% |
| Qwen3 8BQ4_K_M | Runs great | est. 105.0 tok/s92.4–117.6calibrated estimate ±12% | Runs great | 234.3 tok/s8k estimate 201.1 tok/s (177.0–225.2, calibrated estimate ±12%)measured (1 run, 512 context) |
| DeepSeek R1 0528 Qwen3 8BQ4_K_M | Runs great | est. 105.0 tok/s92.4–117.6calibrated estimate ±12% | Runs great | est. 201.1 tok/s177.0–225.2calibrated estimate ±12% |
| DeepSeek R1 Distill Llama 8BQ4_K_M | Runs great | est. 109.3 tok/s96.2–122.4calibrated estimate ±12% | Runs great | est. 209.3 tok/s184.2–234.4calibrated estimate ±12% |
| Kanana 1.5 8BQ4_K_M | Runs great | est. 109.3 tok/s96.2–122.4calibrated estimate ±12% | Runs great | est. 209.3 tok/s184.2–234.4calibrated estimate ±12% |
| Llama 3.1 8BQ4_K_M | Runs greatLlama 3.1 8B verdict on the RTX 3090 | est. 109.3 tok/s96.2–122.4calibrated estimate ±12% | Runs greatLlama 3.1 8B verdict on the RTX 5090 | est. 209.3 tok/s184.2–234.4calibrated estimate ±12% |
| Gemma 4 E4BQ4_K_M | Runs great | est. 161.4 tok/s113.0–209.9theoretical estimate ±30% | Runs great | 234.0 tok/s8k estimate 257.7 tok/s (180.4–335.0, theoretical estimate ±30%)measured (1 run, 512 context) |
| Ling-3.0 TinyQ4_K_M | Runs great | est. 377.2 tok/s264.1–490.4theoretical estimate ±30% | Runs great | est. 485.1 tok/s339.6–630.7theoretical estimate ±30% |
| Gemma 4 E2BQ4_K_M | Runs great | est. 269.1 tok/s188.4–349.8theoretical estimate ±30% | Runs great | 341.8 tok/s8k estimate 386.7 tok/s (270.7–502.7, theoretical estimate ±30%)measured (1 run, 512 context) |
| Qwen3.5 4BQ4_K_M | Runs great | est. 211.5 tok/s186.1–236.8calibrated estimate ±12% | Runs great | est. 404.8 tok/s356.3–453.4calibrated estimate ±12% |
| Nanbeige4.2 3BQ4_K_M | Runs great | est. 106.9 tok/s74.9–139.0theoretical estimate ±30% | Runs great | est. 204.8 tok/s143.3–266.2theoretical estimate ±30% |
| Spark-X2.5 4BQ4_K_M | Runs great | est. 225.8 tok/s198.7–252.9calibrated estimate ±12% | Runs great | est. 432.3 tok/s380.4–484.1calibrated estimate ±12% |
| Llama 3.2 3BQ4_K_M | Runs great | est. 221.4 tok/s194.8–248.0calibrated estimate ±12% | Runs great | est. 423.9 tok/s373.0–474.7calibrated estimate ±12% |
| LFM2.5 2.6BQ4_K_M | Runs great | est. 363.1 tok/s254.2–472.1theoretical estimate ±30% | Runs great | est. 695.3 tok/s486.7–903.8theoretical estimate ±30% |
| MiniCPM5 2BQ4_K_M | Runs great | est. 342.6 tok/s301.5–383.7calibrated estimate ±12% | Runs great | est. 656.0 tok/s577.2–734.7calibrated estimate ±12% |
| HyperCLOVA X SEED 1.5BQ4_K_M | Runs great | est. 360.9 tok/s317.6–404.2calibrated estimate ±12% | Runs great | est. 691.0 tok/s608.1–773.9calibrated estimate ±12% |
| EXAONE 4.0 1.2BQ4_K_M | Runs great | est. 498.9 tok/s439.0–558.8calibrated estimate ±12% | Runs great | est. 955.1 tok/s840.5–1,069.8calibrated estimate ±12% |
| Llama 3.2 1BQ4_K_M | Runs great | est. 607.5 tok/s534.6–680.5calibrated estimate ±12% | Runs great | est. 1,163.2 tok/s1,023.6–1,302.7calibrated estimate ±12% |
| MiniCPM5 1BQ4_K_M | Runs great | est. 735.1 tok/s646.9–823.3calibrated estimate ±12% | Runs great | est. 1,407.3 tok/s1,238.5–1,576.2calibrated estimate ±12% |
| Kolibri 1Q4_K_M | Won't runTry IQ4_XS: Runs well | est. 68.5 tok/s47.9–89.0theoretical estimate ±30% | Runs well | est. 83.3 tok/s58.3–108.3theoretical estimate ±30% |
| Llama 3.3 70BQ4_K_M | Won't runTry IQ4_XS: Runs slowly | est. 2.2 tok/s1.7–2.6calibrated estimate ±20% | Runs slowly | est. 2.7 tok/s2.2–3.3calibrated estimate ±20% |
| GLM-5.3 FlashQ4_K_M | Won't run | — | Won't run | — |
| DeepSeek V4 Flash 0731MXFP4 | Won't run | — | Won't run | — |
| Solar Open 2 250BIQ4_XS | Won't run | — | Won't run | — |
| Ling-3.0 FlashQ4_K_M | Won't run | — | Won't run | — |
| Qwen3.5 122B-A10BQ4_K_M | Won't runTry UD-Q2_K_XL (heavy quality loss): Runs well | est. 41.3 tok/s28.9–53.6theoretical estimate ±30% | Won't runTry UD-Q2_K_XL (heavy quality loss): Runs well | est. 66.4 tok/s46.5–86.3theoretical estimate ±30% |
| Laguna S 2.1Q4_K_M | Won't run | — | Won't run | — |
| gpt-oss-120bMXFP4 | Won't rungpt-oss-120b verdict on the RTX 3090 | — | Won't rungpt-oss-120b verdict on the RTX 5090 | — |
| Solar Open 100BQ4_K_M | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 28.4 tok/s19.9–36.9theoretical estimate ±30% | Won't runTry IQ4_XS: Runs slowly | est. 21.9 tok/s15.3–28.4theoretical estimate ±30% |
Models with a thinking mode spend extra tokens thinking, so their speed thresholds are 1.5× stricter (30 / 12 / 3 tok/s). With thinking turned off, a model may do one grade better.
Public benchmarks
Measured results on the RTX 3090
| Model | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| llama-2-7b | Q4_0 | llama.cpp | 512 | 5,175.0 | 158.2 | — | github.com | 2025-08-01 |
| gpt-oss-20b | MXFP4 | llama.cpp | 2k | 5,143.0 | 162.0 | — | github.com | 2025-08-20 |
| Qwen3 8B | Q4_K_XL | llama.cpp | 16k | — | 87.5 | — | hardware-corner.net | 2026-08-09 |
| Qwen3 30B-A3B (2507) | Q4_K_XL | llama.cpp | 16k | — | 113.8 | — | hardware-corner.net | 2026-08-09 |
| Gemma 4 26B-A4B | Q4_K_M | ollama | 32k | — | 108.0 | FA off | github.com | 2026-04-07 |
| Qwen3 8B | Q4_K_XL | llama.cpp | 4k | 4,050.0 | 115.3 | CUDA | hardware-corner.net | 2025-12-09 |
| Qwen3 32B | Q4_K_XL | llama.cpp | 4k | 1,088.0 | 35.1 | CUDA | hardware-corner.net | 2025-12-09 |
Measured results on the RTX 5090
| Model | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| llama-2-7b | Q4_0 | llama.cpp | 512 | 14,073.0 | 290.0 | — | github.com | 2025-10-23 |
| gpt-oss-20b | MXFP4 | llama.cpp | 2k | 9,841.0 | 282.3 | — | github.com | 2025-08-20 |
| Qwen3 8B | Q4_K_XL | llama.cpp | 16k | — | 145.3 | — | hardware-corner.net | 2026-08-09 |
| Qwen3 30B-A3B (2507) | Q4_K_XL | llama.cpp | 16k | — | 141.6 | — | hardware-corner.net | 2026-08-09 |
| gpt-oss-120b | MXFP4 | llama.cpp | 4k | — | 9.6 | --n-cpu-moe 21, DDR4 server | hardware-corner.net | 2026-08-09 |
| Gemma 4 26B-A4B | Q4_K_M | llama.cpp | 512 | 13,588.0 | 223.8 | CUDA, KV q8_0 | github.com | 2026-05-24 |
| Gemma 4 26B-A4B | Q4_K_M | ollama | 8k | — | 208.4 | CUDA, FA off | github.com | 2026-04-05 |
| Qwen3.8 27B | Q4_K_M | llama.cpp | 1k | 3,972.0 | 81.8 | CUDA | github.com | 2026-09-12 |
| Qwen3 8B | Q4_K_XL | llama.cpp | 4k | 11,933.0 | 200.4 | CUDA | hardware-corner.net | 2026-03-02 |
| Qwen3 32B | Q4_K_XL | llama.cpp | 4k | 2,931.0 | 61.4 | CUDA | hardware-corner.net | 2025-12-09 |
| Qwen3 8B | Q4_K_M | llama.cpp | 512 | — | 234.3 | CUDA, KV q8_0 | github.com | 2026-10-07 |
| Qwen3 8B | Q8_0 | llama.cpp | 512 | 10,787.0 | 136.2 | CUDA | github.com | 2025-10-16 |
| Qwen3 32B | Q4_K_M | llama.cpp | 512 | 3,446.0 | 53.3 | CUDA | github.com | 2026-09-15 |
| Gemma 4 E4B | Q4_K_M | llama.cpp | 512 | — | 234.0 | Vulkan | github.com | 2026-09-08 |
| Gemma 4 E2B | Q4_K_M | llama.cpp | 512 | — | 341.8 | Vulkan | github.com | 2026-09-08 |
Public benchmarks we calibrate against. Their conditions (context, backend, flags) can differ from the estimates above.
Frequently asked questions
Which is faster for local LLMs, the RTX 3090 or the RTX 5090?
The RTX 5090 generates text about 1.9× faster than the RTX 3090 (median of our estimates over the 46 models that fit entirely in memory on both at Q4_K_M or the closest available quant). Estimated speed on Qwen3.5 35B-A3B at Q4_K_M: the RTX 3090 est. 148.8 tok/s (119.1–178.6, calibrated estimate ±20%); the RTX 5090 est. 213.4 tok/s (170.7–256.0, calibrated estimate ±20%).
What is the largest model each can run entirely in memory?
The RTX 3090 fits Qwen3.5 35B-A3B at Q4_K_M (22.63 GB file) entirely in 24 GB, at est. 148.8 tok/s (119.1–178.6, calibrated estimate ±20%). The RTX 5090 fits K2-Horizon MoVA 36B-A4B at Q4_K_M (22.37 GB file) entirely in 32 GB, at est. 149.6 tok/s (104.7–194.5, theoretical estimate ±30%).
How many local LLMs run well on each?
At Q4_K_M with 8k context, out of 58 tracked models: the RTX 3090 — 44 Runs great, 4 Runs well, 0 Runs slowly, 10 Won't run; the RTX 5090 — 48 Runs great, 1 Runs well, 1 Runs slowly, 8 Won't run. 6 models get a different verdict.
Speeds are estimates from memory bandwidth, calibrated against public benchmarks, and each one comes with an error band and a confidence label. Real results vary with drivers, backend, context length and thermals.