RTX 5070 Ti vs RTX 5060 Ti 16GB 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
Both cards have 15.4 GB usable in CanRun's count, so the same models fit fully on both. Mistral Small 3.2 24B needs 15.9 GB, so part spills to system RAM, yet it runs well on both. DeepSeek R1 Distill Qwen 14B gets different verdicts, but not because of memory. It always thinks, so its speed bar is 1.5× stricter. On the RTX 5060 Ti 16GB its estimate lands just under that bar, so it runs well; on the RTX 5070 Ti it runs great. Qwen3 14B, judged by the same bar, lands just over it on the RTX 5060 Ti 16GB and runs great. Their estimates there are nearly equal; the line falls between them.
When a model fits on the GPU, the speed gap comes from memory bandwidth. Once part spills, the share in system RAM sets the pace; with the same DDR5 assumed on both, the gap shrinks a lot. At Q4_K_M, Gemma 3 27B and Qwen3 32B don't fit on either GPU and run slowly on both.
Despite the name, the GeForce RTX 5070 Ti Laptop is a different GPU, with 12 GB of VRAM and 672 GB/s. gpt-oss-20b runs great fully on the desktop card, but on the laptop some experts go to system RAM. Gemma 4 26B-A4B at IQ4_XS splits the same way.
Speed
The RTX 5070 Ti generates text about 2× faster than the RTX 5060 Ti 16GB (median of our estimates over the 29 models that fit entirely in memory on both at Q4_K_M or the closest available quant).
- Reference model
- gpt-oss-20b at MXFP4, the largest model that fits entirely in memory on both at Q4_K_M or the closest available quant
- RTX 5070 Ti
- 189.5 tok/s measured at 2k context (1 run; 8k estimate 193.7 tok/s, 155.0–232.5, calibrated estimate ±20%)
- RTX 5060 Ti 16GB
- 111.6 tok/s measured at 2k context (1 run; 8k estimate 104.3 tok/s, 83.5–125.2, calibrated estimate ±20%)
- Prompt processing on gpt-oss-20b
- RTX 5070 Ti: about 6,697 tok/s (4,018–9,376, rough estimate ±40%); RTX 5060 Ti 16GB: about 3,599 tok/s (2,159–5,039, rough estimate ±40%)
Specs side by side
| Spec | RTX 5070 Ti | RTX 5060 Ti 16GB | Difference |
|---|---|---|---|
| Memory | 16 GB | 16 GB | Same |
| Memory bandwidth | 896.0 GB/s | 448.0 GB/s | 2× on the RTX 5070 Ti |
| FP16 compute | 44.1 TFLOPS | 23.7 TFLOPS | 1.86× on the RTX 5070 Ti |
| Launch year | 2025 | 2025 | — |
| Price | $949 street (as of 2026-08-09) | $569 street (as of 2026-08-09) | — |
| Public benchmark results | 3 results | 4 results | — |
Verdicts at a glance
Out of 58 models at Q4_K_M (or the closest available quant) with 8k context.
- RTX 5070 TiRuns great29 modelsRuns well8 modelsRuns slowly11 modelsWon't run10 models
- RTX 5060 Ti 16GBRuns great28 modelsRuns well9 modelsRuns slowly11 modelsWon't run10 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
1 model gets a different verdict on the two.
Scroll sideways to see every column.
| Model | RTX 5070 Ti verdict | RTX 5070 Ti speed | RTX 5060 Ti 16GB verdict | RTX 5060 Ti 16GB speed |
|---|---|---|---|---|
| DeepSeek R1 Distill Qwen 14BQ4_K_M | Runs great | est. 59.2 tok/s52.1–66.3calibrated estimate ±12% | Runs well | est. 29.6 tok/s26.0–33.1calibrated estimate ±12% |
Every model on both
Scroll sideways to see every column.
| Model | RTX 5070 Ti verdict | RTX 5070 Ti speed | RTX 5060 Ti 16GB verdict | RTX 5060 Ti 16GB speed |
|---|---|---|---|---|
| gpt-oss-20bMXFP4 | Runs great | 189.5 tok/s8k estimate 193.7 tok/s (155.0–232.5, calibrated estimate ±20%)measured (1 run, 2k context) | 111.6 tok/s8k estimate 104.3 tok/s (83.5–125.2, calibrated estimate ±20%)measured (1 run, 2k context) | |
| Kanana 1.5 15.7B-A3BQ4_K_M | Runs great | est. 151.6 tok/s121.3–181.9calibrated estimate ±20% | Runs great | est. 89.1 tok/s71.3–106.9calibrated estimate ±20% |
| Qwen3 14BQ4_K_M | Runs great | est. 60.6 tok/s53.4–67.9calibrated estimate ±12% | est. 30.3 tok/s26.7–34.0calibrated estimate ±12% | |
| DeepSeek R1 Distill Qwen 14BQ4_K_M | Runs great | est. 59.2 tok/s52.1–66.3calibrated estimate ±12% | Runs well | est. 29.6 tok/s26.0–33.1calibrated estimate ±12% |
| HyperCLOVA X SEED Think 14BQ4_K_M | Runs great | est. 61.5 tok/s43.1–80.0theoretical estimate ±30% | Runs great | est. 30.8 tok/s21.5–40.0theoretical estimate ±30% |
| Phi-4Q4_K_M | Runs great | est. 58.5 tok/s51.4–65.5calibrated estimate ±12% | Runs great | est. 29.2 tok/s25.7–32.7calibrated estimate ±12% |
| Gemma 3 12BQ4_K_M | Runs great | est. 80.0 tok/s70.4–89.6calibrated estimate ±12% | est. 40.0 tok/s35.2–44.8calibrated estimate ±12% | |
| Mellum2.1 12B-A2.5B ThinkingQ4_K_M | Runs great | est. 235.4 tok/s188.3–282.5calibrated estimate ±20% | Runs great | est. 147.7 tok/s118.2–177.2calibrated estimate ±20% |
| Gemma 4 12BQ4_K_M | Runs great | est. 86.5 tok/s76.1–96.8calibrated estimate ±12% | Runs great | est. 43.2 tok/s38.0–48.4calibrated estimate ±12% |
| Qwen3.5 9BQ4_K_M | Runs great | est. 102.2 tok/s89.9–114.4calibrated estimate ±12% | Runs great | est. 51.1 tok/s45.0–57.2calibrated estimate ±12% |
| K2-Horizon 7BQ4_K_M | Runs great | est. 92.3 tok/s81.2–103.3calibrated estimate ±12% | Runs great | est. 46.1 tok/s40.6–51.7calibrated estimate ±12% |
| Qwen3 8BQ4_K_M | Runs great | est. 100.5 tok/s88.5–112.6calibrated estimate ±12% | Runs great | est. 50.3 tok/s44.2–56.3calibrated estimate ±12% |
| DeepSeek R1 0528 Qwen3 8BQ4_K_M | Runs great | est. 100.5 tok/s88.5–112.6calibrated estimate ±12% | Runs great | est. 50.3 tok/s44.2–56.3calibrated estimate ±12% |
| DeepSeek R1 Distill Llama 8BQ4_K_M | Runs great | est. 104.6 tok/s92.1–117.2calibrated estimate ±12% | Runs great | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% |
| Kanana 1.5 8BQ4_K_M | Runs great | est. 104.6 tok/s92.1–117.2calibrated estimate ±12% | Runs great | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% |
| Llama 3.1 8BQ4_K_M | Runs great | est. 104.6 tok/s92.1–117.2calibrated estimate ±12% | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% | |
| Gemma 4 E4BQ4_K_M | Runs great | est. 156.0 tok/s109.2–202.8theoretical estimate ±30% | Runs great | est. 87.2 tok/s61.0–113.3theoretical estimate ±30% |
| Ling-3.0 TinyQ4_K_M | Runs great | est. 369.6 tok/s258.7–480.4theoretical estimate ±30% | Runs great | est. 250.3 tok/s175.2–325.4theoretical estimate ±30% |
| Gemma 4 E2BQ4_K_M | Runs great | est. 261.7 tok/s183.2–340.2theoretical estimate ±30% | Runs great | est. 158.9 tok/s111.2–206.6theoretical estimate ±30% |
| Qwen3.5 4BQ4_K_M | Runs great | est. 202.4 tok/s178.1–226.7calibrated estimate ±12% | Runs great | est. 101.2 tok/s89.1–113.4calibrated estimate ±12% |
| Nanbeige4.2 3BQ4_K_M | Runs great | est. 102.4 tok/s71.7–133.1theoretical estimate ±30% | Runs great | est. 51.2 tok/s35.8–66.5theoretical estimate ±30% |
| Spark-X2.5 4BQ4_K_M | Runs great | est. 216.1 tok/s190.2–242.1calibrated estimate ±12% | Runs great | est. 108.1 tok/s95.1–121.0calibrated estimate ±12% |
| Llama 3.2 3BQ4_K_M | Runs great | est. 211.9 tok/s186.5–237.4calibrated estimate ±12% | Runs great | est. 106.0 tok/s93.2–118.7calibrated estimate ±12% |
| LFM2.5 2.6BQ4_K_M | Runs great | est. 347.6 tok/s243.3–451.9theoretical estimate ±30% | Runs great | est. 173.8 tok/s121.7–226.0theoretical estimate ±30% |
| MiniCPM5 2BQ4_K_M | Runs great | est. 328.0 tok/s288.6–367.3calibrated estimate ±12% | Runs great | est. 164.0 tok/s144.3–183.7calibrated estimate ±12% |
| HyperCLOVA X SEED 1.5BQ4_K_M | Runs great | est. 345.5 tok/s304.0–387.0calibrated estimate ±12% | Runs great | est. 172.8 tok/s152.0–193.5calibrated estimate ±12% |
| EXAONE 4.0 1.2BQ4_K_M | Runs great | est. 477.6 tok/s420.3–534.9calibrated estimate ±12% | Runs great | est. 238.8 tok/s210.1–267.4calibrated estimate ±12% |
| Llama 3.2 1BQ4_K_M | Runs great | est. 581.6 tok/s511.8–651.4calibrated estimate ±12% | Runs great | est. 290.8 tok/s255.9–325.7calibrated estimate ±12% |
| MiniCPM5 1BQ4_K_M | Runs great | est. 703.7 tok/s619.2–788.1calibrated estimate ±12% | Runs great | est. 351.8 tok/s309.6–394.1calibrated estimate ±12% |
| K2-Horizon MoVA 36B-A4BQ4_K_M | Runs well | est. 38.6 tok/s27.0–50.2theoretical estimate ±30% | Runs well | est. 30.7 tok/s21.5–39.9theoretical estimate ±30% |
| Qwen3.5 35B-A3BQ4_K_M | Runs well | est. 83.5 tok/s58.5–108.6theoretical estimate ±30% | Runs well | est. 62.4 tok/s43.7–81.1theoretical estimate ±30% |
| Qwen3.6 35B-A3BQ4_K_M | Runs well | est. 91.1 tok/s63.8–118.5theoretical estimate ±30% | Runs well | est. 69.5 tok/s48.7–90.4theoretical estimate ±30% |
| MiniCPM-V 4.7 35B-A3BQ4_K_M | Runs well | est. 97.0 tok/s67.9–126.1theoretical estimate ±30% | Runs well | est. 74.7 tok/s52.3–97.1theoretical estimate ±30% |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | Runs well | est. 62.2 tok/s43.5–80.8theoretical estimate ±30% | Runs well | est. 50.8 tok/s35.6–66.0theoretical estimate ±30% |
| Qwen3 30B-A3B (2507)Q4_K_M | Runs well | est. 74.3 tok/s52.0–96.6theoretical estimate ±30% | est. 57.7 tok/s40.4–75.1theoretical estimate ±30% | |
| Gemma 4 26B-A4BQ4_K_M | Runs well | est. 87.9 tok/s61.5–114.3theoretical estimate ±30% | Runs well | est. 59.2 tok/s41.4–76.9theoretical estimate ±30% |
| Mistral Small 3.2 24BQ4_K_M | Runs well | est. 22.6 tok/s18.1–27.2calibrated estimate ±20% | Runs well | est. 14.8 tok/s11.8–17.7calibrated estimate ±20% |
| EXAONE 4.5 33BQ4_K_M | Runs slowly | est. 6.4 tok/s5.1–7.7calibrated estimate ±20% | Runs slowly | est. 5.6 tok/s4.5–6.7calibrated estimate ±20% |
| DeepSeek R1 Distill Qwen 32BQ4_K_M | Runs slowly | est. 5.0 tok/s4.0–6.0calibrated estimate ±20% | Runs slowly | est. 4.5 tok/s3.6–5.4calibrated estimate ±20% |
| Qwen3 32BQ4_K_M | Runs slowly | est. 5.1 tok/s4.0–6.1calibrated estimate ±20% | Runs slowly | est. 4.5 tok/s3.6–5.5calibrated estimate ±20% |
| EXAONE 4.0 32BQ4_K_M | Runs slowly | est. 7.2 tok/s5.7–8.6calibrated estimate ±20% | Runs slowly | est. 6.1 tok/s4.9–7.4calibrated estimate ±20% |
| Gemma 4 31BQ4_K_M | Runs slowly | est. 8.3 tok/s6.7–10.0calibrated estimate ±20% | Runs slowly | est. 7.0 tok/s5.6–8.4calibrated estimate ±20% |
| Muse Glimmer 30BQ4_K_M | Runs slowly | est. 12.2 tok/s9.8–14.7calibrated estimate ±20% | Runs slowly | est. 9.5 tok/s7.6–11.4calibrated estimate ±20% |
| Granite 4.2 30BQ4_K_M | Runs slowly | est. 6.7 tok/s5.4–8.1calibrated estimate ±20% | Runs slowly | est. 5.8 tok/s4.7–7.0calibrated estimate ±20% |
| Qwen3.5 27BQ4_K_M | Runs slowly | est. 11.3 tok/s9.0–13.6calibrated estimate ±20% | Runs slowly | est. 8.9 tok/s7.1–10.7calibrated estimate ±20% |
| Qwen3.6 27BQ4_K_M | Runs slowly | est. 12.2 tok/s9.8–14.7calibrated estimate ±20% | Runs slowly | est. 9.5 tok/s7.6–11.4calibrated estimate ±20% |
| Qwen3.8 27BQ4_K_M | Runs slowly | est. 10.4 tok/s8.3–12.5calibrated estimate ±20% | Runs slowly | est. 8.4 tok/s6.7–10.0calibrated estimate ±20% |
| Gemma 3 27BQ4_K_M | Runs slowly | est. 12.6 tok/s10.1–15.2calibrated estimate ±20% | Runs slowly | est. 9.7 tok/s7.8–11.7calibrated 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 run | — | Won't run | — |
| Laguna S 2.1Q4_K_M | Won't run | — | Won't run | — |
| gpt-oss-120bMXFP4 | Won't run | — | — | |
| Solar Open 100BQ4_K_M | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 21.8 tok/s15.2–28.3theoretical estimate ±30% | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 18.7 tok/s13.1–24.4theoretical estimate ±30% |
| Kolibri 1Q4_K_M | Won't runTry Q2_K (heavy quality loss): Runs well | est. 84.6 tok/s59.2–110.0theoretical estimate ±30% | Won't runTry Q2_K (heavy quality loss): Runs well | est. 63.3 tok/s44.3–82.3theoretical estimate ±30% |
| Llama 3.3 70BQ4_K_M | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 2.7 tok/s2.2–3.2calibrated estimate ±20% | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 2.5 tok/s2.0–3.1calibrated estimate ±20% |
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 5070 Ti
| Model | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| llama-2-7b | Q4_0 | llama.cpp | 512 | 6,952.0 | 176.9 | — | github.com | 2025-12-07 |
| gpt-oss-20b | MXFP4 | llama.cpp | 2k | 6,326.0 | 189.5 | — | github.com | 2025-08-20 |
| Qwen3 8B | Q4_K_XL | llama.cpp | 4k | 5,557.0 | 120.5 | CUDA | hardware-corner.net | 2025-12-09 |
Measured results on the RTX 5060 Ti 16GB
| Model | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| llama-2-7b | Q4_0 | llama.cpp | 512 | 3,737.0 | 90.9 | — | github.com | 2025-08-02 |
| gpt-oss-20b | MXFP4 | llama.cpp | 2k | 3,821.0 | 111.6 | — | github.com | 2025-08-20 |
| Qwen3.8 27B | UD-IQ4_XS | llama.cpp | 8k | — | 27.6 | KV q8_0 | github.com | 2026-10-11 |
| Qwen3 8B | Q4_K_XL | llama.cpp | 4k | 2,965.0 | 69.2 | CUDA | hardware-corner.net | 2025-12-09 |
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 5070 Ti or the RTX 5060 Ti 16GB?
The RTX 5070 Ti generates text about 2× faster than the RTX 5060 Ti 16GB (median of our estimates over the 29 models that fit entirely in memory on both at Q4_K_M or the closest available quant). Estimated speed on gpt-oss-20b at MXFP4: the RTX 5070 Ti 189.5 tok/s measured at 2k context (1 run; 8k estimate 193.7 tok/s, 155.0–232.5, calibrated estimate ±20%); the RTX 5060 Ti 16GB 111.6 tok/s measured at 2k context (1 run; 8k estimate 104.3 tok/s, 83.5–125.2, calibrated estimate ±20%).
What is the largest model each can run entirely in memory?
The RTX 5070 Ti fits Muse Glimmer 30B at IQ4_XS (15.44 GB file) entirely in 16 GB, at est. 40.3 tok/s (35.5–45.2, calibrated estimate ±12%). The RTX 5060 Ti 16GB fits Muse Glimmer 30B at IQ4_XS (15.44 GB file) entirely in 16 GB, at est. 20.2 tok/s (17.7–22.6, calibrated estimate ±12%).
How many local LLMs run well on each?
At Q4_K_M with 8k context, out of 58 tracked models: the RTX 5070 Ti — 29 Runs great, 8 Runs well, 11 Runs slowly, 10 Won't run; the RTX 5060 Ti 16GB — 28 Runs great, 9 Runs well, 11 Runs slowly, 10 Won't run. 1 model gets 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.