Apple M5 Pro
The Apple M5 Pro ships in 24–64 GB unified-memory configurations at 307 GB/s. Across those configurations it runs 56 of our 84 tracked models natively in VRAM at 8k context.
More memory means more of our tracked models fit natively — see which configuration you need below.
| Configuration | Bandwidth | CPU cores | Native models | + Offload |
|---|---|---|---|---|
| 64 GB | 307 GB/s | 18 (6S + 12P) | 56 / 84 | 0 |
| 48 GB | 307 GB/s | 18 (6S + 12P) | 51 / 84 | 0 |
| 24 GB | 307 GB/s | 15 (5S + 10P) | 39 / 84 | 0 |
How much of the Apple M5 Pro's memory is actually usable?
macOS and background apps need a slice of the pool before a model gets to use it — this site reserves 8GB on every unified-memory GPU, the same baseline used everywhere else on this site. What's left is real headroom for a model's weights and KV cache:
M5 Pro's bandwidth climb, from its own vantage point
M3 Pro cut memory bandwidth 25% below what M1 Pro and M2 Pro had already shipped, and M4 Pro reversed that hard (see the M3 Pro page's own bandwidth note for that story). M5 Pro keeps the recovery going. Tracking the Pro tier's bandwidth across five generations:
M1 Pro and M2 Pro both shipped at 200 GB/s — no change across a full generation. M3 Pro then dropped 25% to 150 GB/s, a real regression, before M4 Pro recovered to 273 GB/s. M5 Pro (this page) adds another 12.5% on top of that, to 307 GB/s — 53.5% ahead of where M1 Pro and M2 Pro left off, and the second straight generation of real gains after the M3 Pro dip. Since decode is bandwidth-bound, that 12.5% shows up directly in tokens/sec: this site's calculator measures Llama 3.3 70B at 8.4 tok/s on the 48GB M5 Pro configuration versus 7.4 tok/s on the identically-sized M4 Pro 48GB.
Apple M5 Pro (64GB)
With 64 GB LPDDR5X at 307 GB/s, this configuration runs 56 models natively. It handles 70B-class models at Q4 quantization.
Apple M5 Pro (64GB): the maximum-memory M5 Pro configuration, announced March 3, 2026 for the 14-inch and 16-inch MacBook Pro on the full 18-core CPU (6 super cores + 12 performance cores)/20-core GPU die at 307 GB/s — the same bandwidth as the 48GB build, since Apple doesn't cut bandwidth for the smaller M5 Pro die. It's also a new tier: M4 Pro topped out at 48GB, so 64GB is this generation's first Pro-tier memory increase.
With 56 GB of real headroom, this is the first M5 Pro configuration where large MoE models really stretch out: Qwen 3.5 122B-A10B fits at Q2_K (54.47 GB, 16.5 tok/s) and Nemotron 3 Super 120B reaches Q2_K (52.03 GB, 15.4 tok/s) — both too large for any smaller M5 Pro tier. Dense 70B-class models clear a real quantization too: Llama 3.3 70B and Qwen2.5 72B both reach Q4_K_M (50.75 GB and 52.12 GB) at 5.4 and 5.3 tok/s. 56 of the 84 tracked models fit natively — the same count as the M5 Max 64GB, at exactly half the speed on every shared fit, since the M5 Max 64GB's 614 GB/s is exactly double this configuration's 307 GB/s.
MLX and llama.cpp's Metal backend are both fully supported. At the same capacity as the M5 Max 64GB but half the bandwidth, this configuration reads as the capacity-first choice within the M5 Pro line — the M5 Max 64GB fits the identical model list at 2x the decode speed, for a real price premium.
Models the 64 GB configuration runs natively (56)
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q2_K · ~16.5 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q2_K · ~15.4 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q2_K · ~10.1 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q2_K · ~14.6 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q2_K · ~14.6 t/s
Show 51 more
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q4_K_M · ~5.3 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q4_K_M · ~5.4 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q4_K_M · ~5.4 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q4_K_M · ~5.4 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q6_K · ~6.8 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q8_0 · ~5.1 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q8_0 · ~22.7 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q8_0 · ~6.2 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q8_0 · ~6.4 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~6.8 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~6.7 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4Q8_0 · ~6.7 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~6.7 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~22.2 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2Q8_0 · ~6.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~21.5 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~7.7 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~8.1 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~8.4 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~27.9 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6Q8_0 · ~17.7 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~5 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~5.3 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~10.1 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~7.9 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~7.9 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~8.4 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~9.5 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6FP32 · ~4.9 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~9 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0FP32 · ~6.2 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5FP32 · ~6.8 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3FP32 · ~7.4 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0FP32 · ~7.4 t/s
- Qwen3 8B8B · MMLU-Pro 56.7FP32 · ~7.4 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3FP32 · ~8 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0FP32 · ~8.2 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~14.9 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~14.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~13.3 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3FP32 · ~15.1 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~17.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~19.3 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~21.8 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~29.2 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~29.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~39.4 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~47 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~56.8 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~116.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~138.3 t/s
Apple M5 Pro (48GB)
With 48 GB LPDDR5X at 307 GB/s, this configuration runs 51 models natively. It handles 70B-class models at Q4 quantization.
Apple M5 Pro (48GB): the upgraded M5 Pro configuration, available in the 14-inch and 16-inch MacBook Pro, standard on the full 18-core CPU (6 super cores + 12 performance cores)/20-core GPU die, at 307 GB/s — a 12.5% jump over the M4 Pro generation's 273 GB/s and the second-largest single-generation Pro-tier bandwidth increase Apple has shipped, after M4 Pro's own recovery (see this page's bandwidth note for the full multi-generation picture).
With 40 GB of real headroom, this calculator's highest-fitting quant for a dense 70B model here is an aggressive Q2_K (32.88 GB, 8.4 tok/s) — technically native, but a real quality trade-off, and about 13.5% faster than the identically-sized M4 Pro 48GB's 7.4 tok/s on the same fit. MoE models fare far better at this size: Qwen 3.5 35B-A3B reaches near-full Q6_K (32.37 GB, 29.3 tok/s), and Command-R 35B fits at Q5_K_M (39.94 GB, 6.9 tok/s). 51 of the 84 tracked models fit natively.
Full llama.cpp K-quants support and MLX optimization. This configuration fits the identical 51-model list as the M5 Max 48GB but at exactly half the decode speed (307 vs 614 GB/s) — the Max tier's premium here buys bandwidth, not extra model compatibility.
Models the 48 GB configuration runs natively (51)
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q2_K · ~8.2 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q2_K · ~8.4 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q2_K · ~8.4 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q2_K · ~8.4 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q5_K_M · ~7.8 t/s
Show 46 more
- Command-R 35B35B · MMLU-Pro 33.0Q5_K_M · ~6.9 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q6_K · ~29.3 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q6_K · ~8 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q6_K · ~8.1 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q6_K · ~8.7 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q6_K · ~8.5 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4Q6_K · ~8.5 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q6_K · ~8.5 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~22.2 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2Q6_K · ~8.6 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~21.5 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~7.7 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~8.1 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~8.4 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~27.9 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6Q8_0 · ~17.7 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q8_0 · ~9.1 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q8_0 · ~9.6 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~18.9 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~7.9 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~7.9 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~8.4 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~9.5 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~9.6 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~9 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~11.6 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~13.4 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3FP32 · ~7.4 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0FP32 · ~7.4 t/s
- Qwen3 8B8B · MMLU-Pro 56.7FP32 · ~7.4 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3FP32 · ~8 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0FP32 · ~8.2 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~14.9 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~14.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~13.3 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3FP32 · ~15.1 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~17.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~19.3 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~21.8 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~29.2 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~29.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~39.4 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~47 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~56.8 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~116.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~138.3 t/s
Apple M5 Pro (24GB)
With 24 GB LPDDR5X at 307 GB/s, this configuration runs 39 models natively. It comfortably runs 7B–32B models at Q4; 70B-class models typically need CPU offload.
Apple M5 Pro (24GB): the base M5 Pro configuration. On the 14-inch MacBook Pro it's standard on a cut-down 15-core CPU (5 super cores + 10 performance cores)/16-core GPU die; the 16-inch MacBook Pro's base M5 Pro instead ships the full 18-core CPU/20-core GPU die at this same 24GB — but both bins share the identical 307 GB/s memory bandwidth, since Apple doesn't cut bandwidth for the smaller die on this chip.
With 16 GB of real headroom, this configuration is where mixture-of-experts models pull dramatically ahead of same-size dense ones: Qwen 3.5 35B-A3B — a 35B-parameter MoE model — fits at Q2_K (15.12 GB) at 61.7 tok/s, essentially real-time, while the dense Qwen3 32B needs the same Q2_K precision at a near-identical footprint (15.50 GB) but manages only 17.7 tok/s, a 3.5x gap from MoE decode reading far fewer bytes per token. Llama 3.1 8B and Qwen2.5 7B both clear Q8_0, near-full precision, at 25.6 and 28.7 tok/s — about 12.5% faster than the identically-sized M4 Pro 24GB on the same two fits (22.8 and 25.5 tok/s there). 39 of the 84 tracked models fit natively.
MLX and llama.cpp's Metal backend are both mature here. Nothing this configuration fits comes close to the default ~18 GB Metal working-set limit (75% of 24 GB) — the largest fit is around 15.5 GB — so the manual wired_limit override doesn't apply at this capacity.
Models the 24 GB configuration runs natively (39)
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q2_K · ~61.7 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q2_K · ~17.7 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q2_K · ~57.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q2_K · ~53.2 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q2_K · ~18.3 t/s
Show 34 more
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q2_K · ~20.8 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q3_K_M · ~18.2 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~27.9 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6Q3_K_M · ~38 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q3_K_M · ~19.1 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q3_K_M · ~19.6 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q4_K_M · ~32.7 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q6_K · ~18.2 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q6_K · ~18 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q6_K · ~19.1 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q6_K · ~21.6 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q8_0 · ~17.5 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q6_K · ~18.8 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0Q8_0 · ~19.5 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5Q8_0 · ~25 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3Q8_0 · ~25.6 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0Q8_0 · ~25.6 t/s
- Qwen3 8B8B · MMLU-Pro 56.7Q8_0 · ~25.3 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3Q8_0 · ~28.7 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0Q8_0 · ~28 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~28.9 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~27.3 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~22.7 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~28.3 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~17.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~19.3 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~21.8 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~29.2 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~29.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~39.4 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~47 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~56.8 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~116.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~138.3 t/s
Too large for any Apple M5 Pro configuration (28)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.6 355B
- GLM-4.7 358B
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- Mistral Medium 3.5 128B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
Continue reading
Frequently asked questions
- How much memory does the Apple M5 Pro have?
- The Apple M5 Pro ships in 3 unified-memory configurations: 64 GB, 48 GB, 24 GB, all at 307 GB/s.
- Should I get the 24 GB or 64 GB Apple M5 Pro?
- Both run everything that fits natively in 24 GB. The extra memory in the 64 GB configuration additionally fits Qwen 3.5 122B-A10B (MoE), Nemotron 3 Super 120B, Llama 4 Scout 109B, and 14 more models natively in VRAM — worth the upgrade if you plan to run any of those.
- How much VRAM does the Apple M5 Pro (64GB) have?
- The Apple M5 Pro (64GB) has 64 GB of LPDDR5X with 307 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M5 Pro (64GB) best for?
- With 64 GB of VRAM, the Apple M5 Pro (64GB) is ideal for running 70B-class models at Q4 quantization and large MoE models — a workstation sweet spot for local inference.
- What LLMs can the Apple M5 Pro (64GB) run locally?
- The Apple M5 Pro (64GB) can run 56 of the 84 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.3 70B Instruct at Q4_K_M, Llama 3.1 8B Instruct at FP32, Llama 3.2 3B Instruct at FP32.
- Can the Apple M5 Pro (64GB) run Llama 3.3 70B Instruct?
- Yes. The Apple M5 Pro (64GB) runs Llama 3.3 70B Instruct natively in VRAM at Q4_K_M quantization, achieving approximately 5.4 tokens per second.
Show 14 more questions
- Can the Apple M5 Pro (64GB) run Qwen 3.6 27B?
- Yes. The Apple M5 Pro (64GB) runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 8.4 tokens per second.
- Can the Apple M5 Pro (64GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M5 Pro (64GB) runs Llama 3.1 8B Instruct natively in VRAM at FP32 quantization, achieving approximately 7.4 tokens per second.
- How much VRAM does the Apple M5 Pro (48GB) have?
- The Apple M5 Pro (48GB) has 48 GB of LPDDR5X with 307 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M5 Pro (48GB) best for?
- With 48 GB of VRAM, the Apple M5 Pro (48GB) is ideal for running 70B-class models at Q4 quantization and large MoE models — a workstation sweet spot for local inference.
- What LLMs can the Apple M5 Pro (48GB) run locally?
- The Apple M5 Pro (48GB) can run 51 of the 84 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.3 70B Instruct at Q2_K, Llama 3.1 8B Instruct at FP32, Llama 3.2 3B Instruct at FP32.
- Can the Apple M5 Pro (48GB) run Llama 3.3 70B Instruct?
- Yes. The Apple M5 Pro (48GB) runs Llama 3.3 70B Instruct natively in VRAM at Q2_K quantization, achieving approximately 8.4 tokens per second.
- Can the Apple M5 Pro (48GB) run Qwen 3.6 27B?
- Yes. The Apple M5 Pro (48GB) runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 8.4 tokens per second.
- Can the Apple M5 Pro (48GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M5 Pro (48GB) runs Llama 3.1 8B Instruct natively in VRAM at FP32 quantization, achieving approximately 7.4 tokens per second.
- How much VRAM does the Apple M5 Pro (24GB) have?
- The Apple M5 Pro (24GB) has 24 GB of LPDDR5X with 307 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M5 Pro (24GB) best for?
- With 24 GB of VRAM, the Apple M5 Pro (24GB) is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.
- What LLMs can the Apple M5 Pro (24GB) run locally?
- The Apple M5 Pro (24GB) can run 39 of the 84 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at Q8_0, Llama 3.2 3B Instruct at FP32, Llama 3.2 1B Instruct at FP32.
- Can the Apple M5 Pro (24GB) run Llama 3.3 70B Instruct?
- The Apple M5 Pro (24GB) does not have enough VRAM to run Llama 3.3 70B Instruct. You would need more VRAM or a lower quantization level.
- Can the Apple M5 Pro (24GB) run Qwen 3.6 27B?
- Yes. The Apple M5 Pro (24GB) runs Qwen 3.6 27B natively in VRAM at Q3_K_M quantization, achieving approximately 18.2 tokens per second.
- Can the Apple M5 Pro (24GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M5 Pro (24GB) runs Llama 3.1 8B Instruct natively in VRAM at Q8_0 quantization, achieving approximately 25.6 tokens per second.