RTX 5060 Ti 16GB vs RTX 5070 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
CanRun counts about 11.4 GB free on the RTX 5070 and 15.4 GB on the RTX 5060 Ti 16GB. gpt-oss-20b needs 12.3 GB at 8k, so it sits entirely on the RTX 5060 Ti 16GB and runs great (a public benchmark result). On the RTX 5070, CanRun keeps some experts in system RAM, and it still runs well by a theoretical estimate (±30%).
Context length is where 12GB runs out first. Qwen3 14B runs great on both cards at 8k, though the RTX 5070 has little margin (check ollama ps). At 16k with an f16 KV cache it needs 11.9 GB, so on the RTX 5070 part of it spills to system RAM; a q8_0 KV cache keeps it on the GPU. The RTX 5060 Ti 16GB keeps it on the GPU even at 32k with f16. For 16k or more on it with an f16 cache, aim for 16GB.
Where both fit, the RTX 5070's memory bandwidth makes it faster. Qwen3 30B-A3B (2507) and Qwen3.6 35B-A3B fit entirely on neither card, so CanRun keeps experts in system RAM; by theoretical estimates (±30%), they run well on both. RAM then sets the pace, and the 16GB card keeps more of each model on its GPU, offsetting the bandwidth gap. The estimates come out close, so the summary's gap doesn't apply here.
Speed
The RTX 5070 generates text about 1.5× faster than the RTX 5060 Ti 16GB (median of our estimates over the 27 models that fit entirely in memory on both at Q4_K_M or the closest available quant).
- Reference model
- Qwen3 14B at Q4_K_M, the largest model that fits entirely in memory on both at Q4_K_M or the closest available quant
- RTX 5060 Ti 16GB
- est. 30.3 tok/s (26.7–34.0, calibrated estimate ±12%)
- RTX 5070
- est. 45.5 tok/s (40.0–50.9, calibrated estimate ±12%)
- Prompt processing on Qwen3 14B
- RTX 5060 Ti 16GB: about 1,709 tok/s (1,025–2,392, rough estimate ±40%); RTX 5070: about 2,228 tok/s (1,337–3,119, rough estimate ±40%)
Specs side by side
| Spec | RTX 5060 Ti 16GB | RTX 5070 | Difference |
|---|---|---|---|
| Memory | 16 GB | 12 GB | +4 GB on the RTX 5060 Ti 16GB |
| Memory bandwidth | 448.0 GB/s | 672.0 GB/s | 1.5× on the RTX 5070 |
| FP16 compute | 23.7 TFLOPS | 30.9 TFLOPS | 1.3× on the RTX 5070 |
| Launch year | 2025 | 2025 | — |
| Price | $569 street (as of 2026-08-09) | $629 street (as of 2026-08-09) | — |
| Public benchmark results | 4 results | 3 results | — |
Verdicts at a glance
Out of 58 models at Q4_K_M (or the closest available quant) with 8k context.
- RTX 5060 Ti 16GBRuns great28 modelsRuns well9 modelsRuns slowly11 modelsWon't run10 models
- RTX 5070Runs great27 modelsRuns well8 modelsRuns slowly13 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
5 models get a different verdict on the two.
Scroll sideways to see every column.
| Model | RTX 5060 Ti 16GB verdict | RTX 5060 Ti 16GB speed | RTX 5070 verdict | RTX 5070 speed |
|---|---|---|---|---|
| gpt-oss-20bMXFP4 | 111.6 tok/s8k estimate 104.3 tok/s (83.5–125.2, calibrated estimate ±20%)measured (1 run, 2k context) | Runs wellgpt-oss-20b verdict on the RTX 5070 | est. 91.6 tok/s64.1–119.1theoretical estimate ±30% | |
| Kanana 1.5 15.7B-A3BQ4_K_M | Runs great | est. 89.1 tok/s71.3–106.9calibrated estimate ±20% | Runs well | est. 95.9 tok/s67.1–124.7theoretical estimate ±30% |
| DeepSeek R1 Distill Qwen 14BQ4_K_M | Runs well | est. 29.6 tok/s26.0–33.1calibrated estimate ±12% | Runs great | est. 44.4 tok/s39.0–49.7calibrated estimate ±12% |
| K2-Horizon MoVA 36B-A4BQ4_K_M | Runs well | est. 30.7 tok/s21.5–39.9theoretical estimate ±30% | Runs slowly | est. 29.0 tok/s20.3–37.7theoretical estimate ±30% |
| Mistral Small 3.2 24BQ4_K_M | Runs well | est. 14.8 tok/s11.8–17.7calibrated estimate ±20% | Runs slowly | est. 7.1 tok/s5.7–8.5calibrated estimate ±20% |
Every model on both
Scroll sideways to see every column.
| Model | RTX 5060 Ti 16GB verdict | RTX 5060 Ti 16GB speed | RTX 5070 verdict | RTX 5070 speed |
|---|---|---|---|---|
| gpt-oss-20bMXFP4 | 111.6 tok/s8k estimate 104.3 tok/s (83.5–125.2, calibrated estimate ±20%)measured (1 run, 2k context) | Runs wellgpt-oss-20b verdict on the RTX 5070 | est. 91.6 tok/s64.1–119.1theoretical estimate ±30% | |
| Kanana 1.5 15.7B-A3BQ4_K_M | Runs great | est. 89.1 tok/s71.3–106.9calibrated estimate ±20% | Runs well | est. 95.9 tok/s67.1–124.7theoretical estimate ±30% |
| Qwen3 14BQ4_K_M | est. 30.3 tok/s26.7–34.0calibrated estimate ±12% | Runs greatQwen3 14B verdict on the RTX 5070 | est. 45.5 tok/s40.0–50.9calibrated estimate ±12% | |
| DeepSeek R1 Distill Qwen 14BQ4_K_M | Runs well | est. 29.6 tok/s26.0–33.1calibrated estimate ±12% | Runs great | est. 44.4 tok/s39.0–49.7calibrated estimate ±12% |
| HyperCLOVA X SEED Think 14BQ4_K_M | Runs great | est. 30.8 tok/s21.5–40.0theoretical estimate ±30% | Runs great | est. 46.1 tok/s32.3–60.0theoretical estimate ±30% |
| Phi-4Q4_K_M | Runs great | est. 29.2 tok/s25.7–32.7calibrated estimate ±12% | Runs great | est. 43.8 tok/s38.6–49.1calibrated estimate ±12% |
| Gemma 3 12BQ4_K_M | est. 40.0 tok/s35.2–44.8calibrated estimate ±12% | Runs greatGemma 3 12B verdict on the RTX 5070 | est. 60.0 tok/s52.8–67.2calibrated estimate ±12% | |
| Mellum2.1 12B-A2.5B ThinkingQ4_K_M | Runs great | est. 147.7 tok/s118.2–177.2calibrated estimate ±20% | Runs great | est. 196.5 tok/s157.2–235.8calibrated estimate ±20% |
| Gemma 4 12BQ4_K_M | Runs great | est. 43.2 tok/s38.0–48.4calibrated estimate ±12% | Runs great | est. 64.8 tok/s57.1–72.6calibrated estimate ±12% |
| Qwen3.5 9BQ4_K_M | Runs great | est. 51.1 tok/s45.0–57.2calibrated estimate ±12% | Runs great | est. 76.6 tok/s67.4–85.8calibrated estimate ±12% |
| K2-Horizon 7BQ4_K_M | Runs great | est. 46.1 tok/s40.6–51.7calibrated estimate ±12% | Runs great | est. 69.2 tok/s60.9–77.5calibrated estimate ±12% |
| Qwen3 8BQ4_K_M | Runs great | est. 50.3 tok/s44.2–56.3calibrated estimate ±12% | Runs great | est. 75.4 tok/s66.4–84.5calibrated estimate ±12% |
| DeepSeek R1 0528 Qwen3 8BQ4_K_M | Runs great | est. 50.3 tok/s44.2–56.3calibrated estimate ±12% | Runs great | est. 75.4 tok/s66.4–84.5calibrated estimate ±12% |
| DeepSeek R1 Distill Llama 8BQ4_K_M | Runs great | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% | Runs great | est. 78.5 tok/s69.1–87.9calibrated estimate ±12% |
| Kanana 1.5 8BQ4_K_M | Runs great | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% | Runs great | est. 78.5 tok/s69.1–87.9calibrated estimate ±12% |
| Llama 3.1 8BQ4_K_M | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% | Runs greatLlama 3.1 8B verdict on the RTX 5070 | est. 78.5 tok/s69.1–87.9calibrated estimate ±12% | |
| Gemma 4 E4BQ4_K_M | Runs great | est. 87.2 tok/s61.0–113.3theoretical estimate ±30% | Runs great | est. 123.5 tok/s86.4–160.5theoretical estimate ±30% |
| Ling-3.0 TinyQ4_K_M | Runs great | est. 250.3 tok/s175.2–325.4theoretical estimate ±30% | Runs great | est. 318.9 tok/s223.2–414.6theoretical estimate ±30% |
| Gemma 4 E2BQ4_K_M | Runs great | est. 158.9 tok/s111.2–206.6theoretical estimate ±30% | Runs great | est. 215.2 tok/s150.7–279.8theoretical estimate ±30% |
| Qwen3.5 4BQ4_K_M | Runs great | est. 101.2 tok/s89.1–113.4calibrated estimate ±12% | Runs great | est. 151.8 tok/s133.6–170.0calibrated estimate ±12% |
| Nanbeige4.2 3BQ4_K_M | Runs great | est. 51.2 tok/s35.8–66.5theoretical estimate ±30% | Runs great | est. 76.8 tok/s53.7–99.8theoretical estimate ±30% |
| Spark-X2.5 4BQ4_K_M | Runs great | est. 108.1 tok/s95.1–121.0calibrated estimate ±12% | Runs great | est. 162.1 tok/s142.6–181.5calibrated estimate ±12% |
| Llama 3.2 3BQ4_K_M | Runs great | est. 106.0 tok/s93.2–118.7calibrated estimate ±12% | Runs great | est. 158.9 tok/s139.9–178.0calibrated estimate ±12% |
| LFM2.5 2.6BQ4_K_M | Runs great | est. 173.8 tok/s121.7–226.0theoretical estimate ±30% | Runs great | est. 260.7 tok/s182.5–338.9theoretical estimate ±30% |
| MiniCPM5 2BQ4_K_M | Runs great | est. 164.0 tok/s144.3–183.7calibrated estimate ±12% | Runs great | est. 246.0 tok/s216.5–275.5calibrated estimate ±12% |
| HyperCLOVA X SEED 1.5BQ4_K_M | Runs great | est. 172.8 tok/s152.0–193.5calibrated estimate ±12% | Runs great | est. 259.1 tok/s228.0–290.2calibrated estimate ±12% |
| EXAONE 4.0 1.2BQ4_K_M | Runs great | est. 238.8 tok/s210.1–267.4calibrated estimate ±12% | Runs great | est. 358.2 tok/s315.2–401.2calibrated estimate ±12% |
| Llama 3.2 1BQ4_K_M | Runs great | est. 290.8 tok/s255.9–325.7calibrated estimate ±12% | Runs great | est. 436.2 tok/s383.8–488.5calibrated estimate ±12% |
| MiniCPM5 1BQ4_K_M | Runs great | est. 351.8 tok/s309.6–394.1calibrated estimate ±12% | Runs great | est. 527.8 tok/s464.4–591.1calibrated estimate ±12% |
| K2-Horizon MoVA 36B-A4BQ4_K_M | Runs well | est. 30.7 tok/s21.5–39.9theoretical estimate ±30% | Runs slowly | est. 29.0 tok/s20.3–37.7theoretical estimate ±30% |
| Qwen3.5 35B-A3BQ4_K_M | Runs well | est. 62.4 tok/s43.7–81.1theoretical estimate ±30% | Runs well | est. 63.3 tok/s44.3–82.3theoretical estimate ±30% |
| Qwen3.6 35B-A3BQ4_K_M | Runs well | est. 69.5 tok/s48.7–90.4theoretical estimate ±30% | Runs well | est. 68.6 tok/s48.0–89.2theoretical estimate ±30% |
| MiniCPM-V 4.7 35B-A3BQ4_K_M | Runs well | est. 74.7 tok/s52.3–97.1theoretical estimate ±30% | Runs well | est. 72.5 tok/s50.7–94.2theoretical estimate ±30% |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | Runs well | est. 50.8 tok/s35.6–66.0theoretical estimate ±30% | Runs well | est. 47.5 tok/s33.3–61.8theoretical estimate ±30% |
| Qwen3 30B-A3B (2507)Q4_K_M | est. 57.7 tok/s40.4–75.1theoretical estimate ±30% | est. 50.5 tok/s35.3–65.6theoretical estimate ±30% | ||
| Gemma 4 26B-A4BQ4_K_M | Runs well | est. 59.2 tok/s41.4–76.9theoretical estimate ±30% | Runs well | est. 53.6 tok/s37.5–69.7theoretical estimate ±30% |
| Mistral Small 3.2 24BQ4_K_M | Runs well | est. 14.8 tok/s11.8–17.7calibrated estimate ±20% | Runs slowly | est. 7.1 tok/s5.7–8.5calibrated estimate ±20% |
| EXAONE 4.5 33BQ4_K_M | Runs slowly | est. 5.6 tok/s4.5–6.7calibrated estimate ±20% | Runs slowly | est. 4.1 tok/s3.2–4.9calibrated estimate ±20% |
| DeepSeek R1 Distill Qwen 32BQ4_K_M | Runs slowly | est. 4.5 tok/s3.6–5.4calibrated estimate ±20% | Runs slowly | est. 3.4 tok/s2.7–4.0calibrated estimate ±20% |
| Qwen3 32BQ4_K_M | Runs slowly | est. 4.5 tok/s3.6–5.5calibrated estimate ±20% | Runs slowly | est. 3.4 tok/s2.7–4.1calibrated estimate ±20% |
| EXAONE 4.0 32BQ4_K_M | Runs slowly | est. 6.1 tok/s4.9–7.4calibrated estimate ±20% | Runs slowly | est. 4.3 tok/s3.5–5.2calibrated estimate ±20% |
| Gemma 4 31BQ4_K_M | Runs slowly | est. 7.0 tok/s5.6–8.4calibrated estimate ±20% | Runs slowly | est. 4.7 tok/s3.8–5.7calibrated estimate ±20% |
| Muse Glimmer 30BQ4_K_M | Runs slowly | est. 9.5 tok/s7.6–11.4calibrated estimate ±20% | Runs slowly | est. 5.8 tok/s4.7–7.0calibrated estimate ±20% |
| Granite 4.2 30BQ4_K_M | Runs slowly | est. 5.8 tok/s4.7–7.0calibrated estimate ±20% | Runs slowly | est. 4.0 tok/s3.2–4.9calibrated estimate ±20% |
| Qwen3.5 27BQ4_K_M | Runs slowly | est. 8.9 tok/s7.1–10.7calibrated estimate ±20% | Runs slowly | est. 5.6 tok/s4.5–6.7calibrated estimate ±20% |
| Qwen3.6 27BQ4_K_M | Runs slowly | est. 9.5 tok/s7.6–11.4calibrated estimate ±20% | Runs slowly | est. 5.8 tok/s4.6–6.9calibrated estimate ±20% |
| Qwen3.8 27BQ4_K_M | Runs slowly | est. 8.4 tok/s6.7–10.0calibrated estimate ±20% | Runs slowly | est. 5.3 tok/s4.3–6.4calibrated estimate ±20% |
| Gemma 3 27BQ4_K_M | Runs slowly | est. 9.7 tok/s7.8–11.7calibrated estimate ±20% | Runs slowly | est. 5.8 tok/s4.7–7.0calibrated 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 rungpt-oss-120b verdict on the RTX 5070 | — | |
| Solar Open 100BQ4_K_M | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 18.7 tok/s13.1–24.4theoretical estimate ±30% | Won't run | — |
| Kolibri 1Q4_K_M | Won't runTry Q2_K (heavy quality loss): Runs well | est. 63.3 tok/s44.3–82.3theoretical estimate ±30% | Won't runTry Q2_K (heavy quality loss): Runs well | est. 69.7 tok/s48.8–90.6theoretical estimate ±30% |
| Llama 3.3 70BQ4_K_M | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 2.5 tok/s2.0–3.1calibrated estimate ±20% | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 2.1 tok/s1.7–2.6calibrated 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 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 |
Measured results on the RTX 5070
| Model | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| llama-2-7b | Q4_0 | llama.cpp | 512 | 5,185.0 | 127.5 | — | github.com | 2025-08-20 |
| Qwen3.5 9B | Q8_0 | llama.cpp | 32k | 3,001.0 | 67.3 | — | localmaxxing.com | 2026-09-10 |
| Qwen3 8B | Q4_K_XL | llama.cpp | 4k | 3,488.0 | 85.8 | 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 5060 Ti 16GB or the RTX 5070?
The RTX 5070 generates text about 1.5× faster than the RTX 5060 Ti 16GB (median of our estimates over the 27 models that fit entirely in memory on both at Q4_K_M or the closest available quant). Estimated speed on Qwen3 14B at Q4_K_M: the RTX 5060 Ti 16GB est. 30.3 tok/s (26.7–34.0, calibrated estimate ±12%); the RTX 5070 est. 45.5 tok/s (40.0–50.9, calibrated estimate ±12%).
What is the largest model each can run entirely in memory?
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%). The RTX 5070 fits Kanana 1.5 15.7B-A3B at IQ4_XS (8.71 GB file) entirely in 12 GB, at est. 130.8 tok/s (104.6–157.0, calibrated estimate ±20%).
How many local LLMs run well on each?
At Q4_K_M with 8k context, out of 58 tracked models: the RTX 5060 Ti 16GB — 28 Runs great, 9 Runs well, 11 Runs slowly, 10 Won't run; the RTX 5070 — 27 Runs great, 8 Runs well, 13 Runs slowly, 10 Won't run. 5 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.