M4 Pro 64GB vs RTX 5060 Ti 16GB for local LLMs
- M4 Pro 64GB: Uses its largest memory option, 64 GB, with 8k context and an f16 KV cache.
- RTX 5060 Ti 16GB: 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
At 64GB, CanRun counts about 70% of the M4 Pro's memory as the GPU's share, 44.8 GB, against 15.4 GB free on the RTX 5060 Ti 16GB. Gemma 3 27B fits whole on the Mac and runs well. On the card it spills partly into system RAM and runs slowly, though speed estimates are close: CanRun caps models under 90% on the GPU at that verdict.
Memory sizes alone mislead both ways. Only the Mac mini offers the M4 Pro with 64GB; the MacBook Pro stops at 48GB, enough to run Qwen3.5 35B-A3B great but not to load Llama 3.3 70B at Q4_K_M. The 24GB base configuration gives its GPU about what the card has, and Qwen3.5 35B-A3B does not load there. The card runs it well with experts in system RAM, a theoretical estimate (±30%). The card's VRAM and system RAM add up: with 64GB of RAM it runs gpt-oss-120b slowly, also theoretical, while the 64GB M4 Pro cannot load it. Our page for gpt-oss-120b on the GeForce RTX 5060 Ti 16GB has the commands for that setup. Plan memory around your models, not the device.
Generation follows memory bandwidth. Long prompts widen the gap: with far more FP16 compute, CanRun expects the card to start answering long documents or coding-agent context much sooner. The summary's prompt-processing line gives a rough estimate (±40%).
Speed
The RTX 5060 Ti 16GB generates text about 1.5× faster than the M4 Pro 64GB (median of our estimates over the 29 models that fit entirely in memory on both at Q4_K_M or the closest available quant).
On gpt-oss-20b the two estimate ranges overlap, so the real gap may be smaller.
- 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
- M4 Pro 64GB
- est. 69.9 tok/s (55.9–83.9, 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
- M4 Pro 64GB: about 454 tok/s (272–636, rough estimate ±40%); RTX 5060 Ti 16GB: about 3,599 tok/s (2,159–5,039, rough estimate ±40%)
Specs side by side
| Spec | M4 Pro 64GB | RTX 5060 Ti 16GB | Difference |
|---|---|---|---|
| Memory | 64 GB of unified memory | 16 GB | — |
| Memory bandwidth | 273.0 GB/s | 448.0 GB/s | 1.64× on the RTX 5060 Ti 16GB |
| FP16 compute | 8.1 TFLOPS | 23.7 TFLOPS | 2.93× on the RTX 5060 Ti 16GB |
| Launch year | 2024 | 2025 | — |
| Price | — | $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.
- M4 Pro 64GBRuns great31 modelsRuns well7 modelsRuns slowly12 modelsWon't run8 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
14 models get a different verdict on the two.
Scroll sideways to see every column.
| Model | M4 Pro 64GB verdict | M4 Pro 64GB speed | RTX 5060 Ti 16GB verdict | RTX 5060 Ti 16GB speed |
|---|---|---|---|---|
| K2-Horizon MoVA 36B-A4BQ4_K_M | Runs great | est. 36.8 tok/s25.8–47.8theoretical estimate ±30% | Runs well | est. 30.7 tok/s21.5–39.9theoretical estimate ±30% |
| Qwen3.5 35B-A3BQ4_K_M | Runs great | est. 60.3 tok/s48.3–72.4calibrated estimate ±20% | Runs well | est. 62.4 tok/s43.7–81.1theoretical estimate ±30% |
| Qwen3.6 35B-A3BQ4_K_M | Runs great | est. 68.8 tok/s55.0–82.5calibrated estimate ±20% | Runs well | est. 69.5 tok/s48.7–90.4theoretical estimate ±30% |
| MiniCPM-V 4.7 35B-A3BQ4_K_M | Runs great | est. 74.2 tok/s59.3–89.0calibrated estimate ±20% | Runs well | est. 74.7 tok/s52.3–97.1theoretical estimate ±30% |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | Runs great | est. 68.0 tok/s47.6–88.4theoretical estimate ±30% | Runs well | est. 50.8 tok/s35.6–66.0theoretical estimate ±30% |
| Qwen3 30B-A3B (2507)Q4_K_M | est. 59.2 tok/s47.4–71.1calibrated estimate ±20% | est. 57.7 tok/s40.4–75.1theoretical estimate ±30% | ||
| Gemma 4 26B-A4BQ4_K_M | Runs great | est. 50.2 tok/s40.2–60.2calibrated estimate ±20% | Runs well | est. 59.2 tok/s41.4–76.9theoretical estimate ±30% |
| Qwen3 14BQ4_K_M | Runs wellQwen3 14B verdict on the M4 Pro 64GB | est. 20.1 tok/s16.0–24.1calibrated estimate ±20% | est. 30.3 tok/s26.7–34.0calibrated estimate ±12% | |
| HyperCLOVA X SEED Think 14BQ4_K_M | Runs well | est. 20.4 tok/s14.2–26.5theoretical estimate ±30% | Runs great | est. 30.8 tok/s21.5–40.0theoretical estimate ±30% |
| Phi-4Q4_K_M | Runs well | est. 19.3 tok/s15.5–23.2calibrated estimate ±20% | Runs great | est. 29.2 tok/s25.7–32.7calibrated estimate ±12% |
| Gemma 4 12BQ4_K_M | Runs well | est. 28.6 tok/s22.9–34.3calibrated estimate ±20% | Runs great | est. 43.2 tok/s38.0–48.4calibrated estimate ±12% |
| Gemma 3 27BQ4_K_M | Runs well | est. 12.0 tok/s9.6–14.5calibrated estimate ±20% | Runs slowly | est. 9.7 tok/s7.8–11.7calibrated estimate ±20% |
| Kolibri 1Q4_K_M | Runs slowly | est. 49.5 tok/s34.6–64.3theoretical 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 | Runs slowly | est. 3.0 tok/s2.1–3.9theoretical estimate ±30% | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 2.5 tok/s2.0–3.1calibrated estimate ±20% |
Every model on both
Scroll sideways to see every column.
| Model | M4 Pro 64GB verdict | M4 Pro 64GB speed | RTX 5060 Ti 16GB verdict | RTX 5060 Ti 16GB speed |
|---|---|---|---|---|
| K2-Horizon MoVA 36B-A4BQ4_K_M | Runs great | est. 36.8 tok/s25.8–47.8theoretical estimate ±30% | Runs well | est. 30.7 tok/s21.5–39.9theoretical estimate ±30% |
| Qwen3.5 35B-A3BQ4_K_M | Runs great | est. 60.3 tok/s48.3–72.4calibrated estimate ±20% | Runs well | est. 62.4 tok/s43.7–81.1theoretical estimate ±30% |
| Qwen3.6 35B-A3BQ4_K_M | Runs great | est. 68.8 tok/s55.0–82.5calibrated estimate ±20% | Runs well | est. 69.5 tok/s48.7–90.4theoretical estimate ±30% |
| MiniCPM-V 4.7 35B-A3BQ4_K_M | Runs great | est. 74.2 tok/s59.3–89.0calibrated estimate ±20% | Runs well | est. 74.7 tok/s52.3–97.1theoretical estimate ±30% |
| Nemotron 3.5 Lightning 30B-A3BQ4_K_M | Runs great | est. 68.0 tok/s47.6–88.4theoretical estimate ±30% | Runs well | est. 50.8 tok/s35.6–66.0theoretical estimate ±30% |
| Qwen3 30B-A3B (2507)Q4_K_M | est. 59.2 tok/s47.4–71.1calibrated estimate ±20% | est. 57.7 tok/s40.4–75.1theoretical estimate ±30% | ||
| Gemma 4 26B-A4BQ4_K_M | Runs great | est. 50.2 tok/s40.2–60.2calibrated estimate ±20% | Runs well | est. 59.2 tok/s41.4–76.9theoretical estimate ±30% |
| gpt-oss-20bMXFP4 | Runs greatgpt-oss-20b verdict on the M4 Pro 64GB | est. 69.9 tok/s55.9–83.9calibrated estimate ±20% | 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. 60.6 tok/s48.5–72.8calibrated estimate ±20% | Runs great | est. 89.1 tok/s71.3–106.9calibrated estimate ±20% |
| Qwen3 14BQ4_K_M | Runs wellQwen3 14B verdict on the M4 Pro 64GB | est. 20.1 tok/s16.0–24.1calibrated estimate ±20% | est. 30.3 tok/s26.7–34.0calibrated estimate ±12% | |
| HyperCLOVA X SEED Think 14BQ4_K_M | Runs well | est. 20.4 tok/s14.2–26.5theoretical estimate ±30% | Runs great | est. 30.8 tok/s21.5–40.0theoretical estimate ±30% |
| Phi-4Q4_K_M | Runs well | est. 19.3 tok/s15.5–23.2calibrated estimate ±20% | Runs great | est. 29.2 tok/s25.7–32.7calibrated estimate ±12% |
| Gemma 3 12BQ4_K_M | Runs greatGemma 3 12B verdict on the M4 Pro 64GB | est. 26.5 tok/s21.2–31.8calibrated estimate ±20% | est. 40.0 tok/s35.2–44.8calibrated estimate ±12% | |
| Mellum2.1 12B-A2.5B ThinkingQ4_K_M | Runs great | est. 101.8 tok/s81.4–122.2calibrated estimate ±20% | Runs great | est. 147.7 tok/s118.2–177.2calibrated estimate ±20% |
| Gemma 4 12BQ4_K_M | Runs well | est. 28.6 tok/s22.9–34.3calibrated estimate ±20% | Runs great | est. 43.2 tok/s38.0–48.4calibrated estimate ±12% |
| Qwen3.5 9BQ4_K_M | Runs great | 37.0 tok/s8k estimate 33.8 tok/s (27.0–40.6, calibrated estimate ±20%)measured (1 run, 4k context) | Runs great | est. 51.1 tok/s45.0–57.2calibrated estimate ±12% |
| K2-Horizon 7BQ4_K_M | Runs great | est. 30.5 tok/s24.4–36.6calibrated estimate ±20% | Runs great | est. 46.1 tok/s40.6–51.7calibrated estimate ±12% |
| Qwen3 8BQ4_K_M | Runs great | est. 33.3 tok/s26.6–39.9calibrated estimate ±20% | Runs great | est. 50.3 tok/s44.2–56.3calibrated estimate ±12% |
| DeepSeek R1 0528 Qwen3 8BQ4_K_M | Runs great | est. 33.3 tok/s26.6–39.9calibrated estimate ±20% | Runs great | est. 50.3 tok/s44.2–56.3calibrated estimate ±12% |
| DeepSeek R1 Distill Llama 8BQ4_K_M | Runs great | est. 34.6 tok/s27.7–41.5calibrated estimate ±20% | Runs great | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% |
| Kanana 1.5 8BQ4_K_M | Runs great | est. 34.6 tok/s27.7–41.5calibrated estimate ±20% | Runs great | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% |
| Llama 3.1 8BQ4_K_M | Runs greatLlama 3.1 8B verdict on the M4 Pro 64GB | est. 34.6 tok/s27.7–41.5calibrated estimate ±20% | est. 52.3 tok/s46.0–58.6calibrated estimate ±12% | |
| Gemma 4 E4BQ4_K_M | Runs great | est. 58.7 tok/s41.1–76.3theoretical estimate ±30% | Runs great | est. 87.2 tok/s61.0–113.3theoretical estimate ±30% |
| Ling-3.0 TinyQ4_K_M | Runs great | est. 175.4 tok/s122.8–228.1theoretical estimate ±30% | Runs great | est. 250.3 tok/s175.2–325.4theoretical estimate ±30% |
| Gemma 4 E2BQ4_K_M | Runs great | est. 108.4 tok/s75.9–141.0theoretical estimate ±30% | Runs great | est. 158.9 tok/s111.2–206.6theoretical estimate ±30% |
| Qwen3.5 4BQ4_K_M | Runs great | est. 67.0 tok/s53.6–80.4calibrated estimate ±20% | Runs great | est. 101.2 tok/s89.1–113.4calibrated estimate ±12% |
| Nanbeige4.2 3BQ4_K_M | Runs great | est. 33.9 tok/s23.7–44.0theoretical estimate ±30% | Runs great | est. 51.2 tok/s35.8–66.5theoretical estimate ±30% |
| Spark-X2.5 4BQ4_K_M | Runs great | est. 71.5 tok/s57.2–85.8calibrated estimate ±20% | Runs great | est. 108.1 tok/s95.1–121.0calibrated estimate ±12% |
| Llama 3.2 3BQ4_K_M | Runs great | est. 70.1 tok/s56.1–84.1calibrated estimate ±20% | Runs great | est. 106.0 tok/s93.2–118.7calibrated estimate ±12% |
| LFM2.5 2.6BQ4_K_M | Runs great | est. 115.0 tok/s80.5–149.5theoretical estimate ±30% | Runs great | est. 173.8 tok/s121.7–226.0theoretical estimate ±30% |
| MiniCPM5 2BQ4_K_M | Runs great | est. 108.5 tok/s86.8–130.2calibrated estimate ±20% | Runs great | est. 164.0 tok/s144.3–183.7calibrated estimate ±12% |
| HyperCLOVA X SEED 1.5BQ4_K_M | Runs great | est. 114.3 tok/s91.4–137.2calibrated estimate ±20% | Runs great | est. 172.8 tok/s152.0–193.5calibrated estimate ±12% |
| EXAONE 4.0 1.2BQ4_K_M | Runs great | est. 158.0 tok/s126.4–189.6calibrated estimate ±20% | Runs great | est. 238.8 tok/s210.1–267.4calibrated estimate ±12% |
| Llama 3.2 1BQ4_K_M | Runs great | est. 192.4 tok/s153.9–230.9calibrated estimate ±20% | Runs great | est. 290.8 tok/s255.9–325.7calibrated estimate ±12% |
| MiniCPM5 1BQ4_K_M | Runs great | est. 232.8 tok/s186.2–279.3calibrated estimate ±20% | Runs great | est. 351.8 tok/s309.6–394.1calibrated estimate ±12% |
| Gemma 3 27BQ4_K_M | Runs well | est. 12.0 tok/s9.6–14.5calibrated estimate ±20% | Runs slowly | est. 9.7 tok/s7.8–11.7calibrated estimate ±20% |
| Mistral Small 3.2 24BQ4_K_M | Runs well | est. 13.2 tok/s10.6–15.9calibrated estimate ±20% | Runs well | est. 14.8 tok/s11.8–17.7calibrated estimate ±20% |
| DeepSeek R1 Distill Qwen 14BQ4_K_M | Runs well | est. 19.6 tok/s15.7–23.5calibrated estimate ±20% | Runs well | est. 29.6 tok/s26.0–33.1calibrated estimate ±12% |
| Kolibri 1Q4_K_M | Runs slowly | est. 49.5 tok/s34.6–64.3theoretical 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 | Runs slowly | est. 3.0 tok/s2.1–3.9theoretical estimate ±30% | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 2.5 tok/s2.0–3.1calibrated estimate ±20% |
| EXAONE 4.5 33BQ4_K_M | Runs slowly | est. 10.1 tok/s8.1–12.1calibrated estimate ±20% | Runs slowly | est. 5.6 tok/s4.5–6.7calibrated estimate ±20% |
| DeepSeek R1 Distill Qwen 32BQ4_K_M | Runs slowly | est. 9.4 tok/s7.5–11.3calibrated estimate ±20% | Runs slowly | est. 4.5 tok/s3.6–5.4calibrated estimate ±20% |
| Qwen3 32BQ4_K_M | Runs slowly | est. 9.5 tok/s7.6–11.4calibrated estimate ±20% | Runs slowly | est. 4.5 tok/s3.6–5.5calibrated estimate ±20% |
| EXAONE 4.0 32BQ4_K_M | Runs slowly | est. 10.4 tok/s8.4–12.5calibrated estimate ±20% | Runs slowly | est. 6.1 tok/s4.9–7.4calibrated estimate ±20% |
| Gemma 4 31BQ4_K_M | Runs slowly | est. 10.9 tok/s8.7–13.1calibrated estimate ±20% | Runs slowly | est. 7.0 tok/s5.6–8.4calibrated estimate ±20% |
| Muse Glimmer 30BQ4_K_M | Runs slowly | est. 11.9 tok/s9.5–14.3calibrated estimate ±20% | Runs slowly | est. 9.5 tok/s7.6–11.4calibrated estimate ±20% |
| Granite 4.2 30BQ4_K_M | Runs slowly | est. 10.4 tok/s8.4–12.5calibrated estimate ±20% | Runs slowly | est. 5.8 tok/s4.7–7.0calibrated estimate ±20% |
| Qwen3.5 27BQ4_K_M | Runs slowly | 9.9 tok/s8k estimate 11.8 tok/s (9.4–14.1, calibrated estimate ±20%)measured (1 run, 512 context) | Runs slowly | est. 8.9 tok/s7.1–10.7calibrated estimate ±20% |
| Qwen3.6 27BQ4_K_M | Runs slowly | est. 12.0 tok/s9.6–14.3calibrated estimate ±20% | Runs slowly | est. 9.5 tok/s7.6–11.4calibrated estimate ±20% |
| Qwen3.8 27BQ4_K_M | Runs slowly | est. 11.5 tok/s9.2–13.8calibrated estimate ±20% | Runs slowly | est. 8.4 tok/s6.7–10.0calibrated 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 runTry UD-Q2_K_XL (heavy quality loss): Runs great | est. 34.1 tok/s27.2–40.9calibrated estimate ±20% | Won't run | — |
| Laguna S 2.1Q4_K_M | Won't run | — | Won't run | — |
| gpt-oss-120bMXFP4 | — | — | ||
| Solar Open 100BQ4_K_M | Won't runTry IQ4_XS: Runs slowly | est. 14.5 tok/s10.1–18.8theoretical estimate ±30% | Won't runTry Q2_K (heavy quality loss): Runs slowly | est. 18.7 tok/s13.1–24.4theoretical 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 M4 Pro 64GB
| Model | Quant | Backend | Context | Prompt (tok/s) | Generation (tok/s) | Flags | Source | Measured |
|---|---|---|---|---|---|---|---|---|
| llama-2-7b | Q4_0 | llama.cpp | 512 | 440.0 | 50.7 | Metal, b1550 (2023) | github.com | 2024-11-08 |
| Qwen3.5 27B | Q4_K_M | llama.cpp | 512 | 115.0 | 9.9 | Metal | github.com | 2026-07-18 |
| Qwen3.5 9B | Q4_K_M | llama.cpp | 4k | — | 37.0 | — | github.com | 2026-07-29 |
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 M4 Pro 64GB or the RTX 5060 Ti 16GB?
The RTX 5060 Ti 16GB generates text about 1.5× faster than the M4 Pro 64GB (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 M4 Pro 64GB est. 69.9 tok/s (55.9–83.9, 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 M4 Pro 64GB fits Kolibri 1 at IQ4_XS (43.63 GB file) entirely in 64 GB of unified memory, at est. 52.1 tok/s (41.7–62.5, calibrated estimate ±20%). 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 M4 Pro 64GB — 31 Runs great, 7 Runs well, 12 Runs slowly, 8 Won't run; the RTX 5060 Ti 16GB — 28 Runs great, 9 Runs well, 11 Runs slowly, 10 Won't run. 14 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.