Can MacBook Pro 14/16 M5 Max 128GB run Flint 40B-A2.5B?
Estimated speed and memory fit for Flint 40B-A2.5B (39.79B MoE, 2.5B active) on MacBook Pro 14/16 M5 Max 128GB. We have no measured run for this pair yet, so the speed is an estimate; submit yours.
Short answer
Yes. Flint 40B-A2.5B Q8_0 fits fully on MacBook Pro 14/16 M5 Max 128GB at 16k context, using 42.8 of 89.1 GB usable, and generates about ~86 tokens/s (estimated, not measured). For Q8_0, our bandwidth-only estimate was ~127 tok/s against a measured median of ~86 (48% too optimistic).
Measured generation speed by quantization
No measured rows for Flint 40B-A2.5B on MacBook Pro 14/16 M5 Max 128GB yet. The ~86 tok/s above is a bandwidth estimate (614 GB/s × 0.77 efficiency). Have this machine? Submit a llama-bench result and this page will switch to measured data.
Generation = tg128 (128 generated tokens, short context). Public rows are medians of community llama.cpp results (sources); our own runs use llama-bench on a rented GPU.
Alternatives
Less memory that still fits Flint 40B-A2.5B
Smaller memory usually means a cheaper card or Mac (we do not list prices).
- RTX 5090 32GB: UD-Q5_K_XL, 31 GB usable, ~391 tok/s scaled from measured
- MacBook Pro 14/16 M5 Pro 48GB: UD-Q5_K_XL, 33 GB usable, ~89 tok/s estimated
- Mac mini (2024) M4 Pro 64GB: Q8_0, 44 GB usable, ~47 tok/s estimated
Faster for Flint 40B-A2.5B
- 2x RTX 3090 (48GB total): Q8_0, 46 GB usable, ~191 tok/s estimated
- MacBook Pro 14/16 (2024) M4 Max 128GB: Q8_0, 89 GB usable, ~122 tok/s scaled from measured
- RTX PRO 6000 96GB: Q8_0, 95 GB usable, ~215 tok/s scaled from measured
Other models on MacBook Pro 14/16 M5 Max 128GB, similar size
- Qwen3.6 35B-A3B (hardware page): Q8_0, fits fully, ~71 tok/s (measured)
- Ornith 1.5 35B-A3B (hardware page): Q8_0, fits fully, ~72 tok/s (scaled from measured)
- Laguna XS 2.1 (hardware page): Q8_0, fits fully, ~71 tok/s (scaled from measured)
- Nanbeige4.3 33B (hardware page): Q6_K, fits fully, ~17 tok/s (scaled from measured)
Memory and context limits
Q8_0 weights are 42.3 GB; the 16k f16 KV cache adds 0.51 GB. Largest context that fits in 89.1 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | 256k |
| q8_0 | 256k |
| q4_0 | 256k |
Native limit 256k. Full KV cache table for Flint 40B-A2.5B.
Copy-paste commands
The advisor's pick for this hardware: Q8_0, 16k context.
llama-server -hf unsloth/flint-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/flint-GGUF:Q8_0
MLX: no MLX conversion yet for this model (none verified in mlx-community), so use llama.cpp or ollama above.
MLX on this Mac
MLX is Apple's machine-learning framework for Apple Silicon. Where an mlx-community conversion exists, the commands above include an MLX block next to llama.cpp and ollama.
- File size. MLX's default quantization is affine with a group size of 64, and every group stores a scale and a bias (MLX docs). With 16-bit scales and biases that works out to about 4.5 bits per weight at 4-bit and 8.5 at 8-bit. llama.cpp lists Q4_K_M at about 4.89 and Q8_0 at about 8.50 bits per weight (llama.cpp quantize README). So an MLX 4-bit repo is usually a little smaller than Q4_K_M and MLX 8-bit is about the same size as Q8_0. Real repos differ because some layers (embeddings, vision towers, MoE experts in MXFP4 for gpt-oss) are stored differently, so we show each repo's actual size.
- Quality. They are different schemes. MLX 4-bit applies one bit width with per-group scale and bias, while Q4_K_M is a K-quant mix that keeps some tensors at higher precision (llama.cpp k-quants PR). We have not found a rigorous like-for-like quality comparison, so we only pair them by bit class: MLX 4-bit with Q4-class GGUF and MLX 8-bit with Q8_0.
- Speed. MLX speeds are not shown on this site yet. Published comparisons often find MLX faster than llama.cpp on Macs: an M2 Ultra study measured about 230 tok/s for MLX against about 150 for llama.cpp (short context) on Qwen-2.5 models (arXiv 2511.05502), and an M4 Max table shows mlx-lm ahead of llama.cpp on most 4-bit text models but behind on Gemma 3 4B (arXiv 2601.19139). So MLX may well run faster than the llama.cpp numbers shown here, but not for every model, and we do not estimate by how much.
- Which package. Text-only conversions run with mlx-lm. Many recent mlx-community repos (Gemma 4, Qwen3.5/3.6/3.8, Qwen3-VL) were converted with mlx-vlm and their model cards say to run them with it, so the command we show follows each repo's card.
Repos are checked against the Hugging Face API (how).
FAQ
How fast is Flint 40B-A2.5B on MacBook Pro 14/16 M5 Max 128GB?
About 85.6 tokens/s for generation (Q8_0, estimated from 614 GB/s memory bandwidth). Speed drops as the context fills; see the depth table on this hardware's other pages.
How much memory does Flint 40B-A2.5B need on MacBook Pro 14/16 M5 Max 128GB?
The advisor's pick, Q8_0, is 42.3 GB of weights plus 0.51 GB of KV cache at 16k context (42.8 GB), against 89.1 GB usable.
Is the Flint 40B-A2.5B speed on MacBook Pro 14/16 M5 Max 128GB measured or estimated?
Estimated. No measured llama.cpp run for this pair is on file yet, so the figure comes from memory bandwidth and our calibration against measured hardware.
What can I use instead of MacBook Pro 14/16 M5 Max 128GB for Flint 40B-A2.5B?
With less memory, RTX 5090 32GB (~391 tok/s), MacBook Pro 14/16 M5 Pro 48GB (~89 tok/s), Mac mini (2024) M4 Pro 64GB (~47 tok/s) also fit it fully. Faster: 2x RTX 3090 (48GB total) (~191 tok/s), MacBook Pro 14/16 (2024) M4 Max 128GB (~122 tok/s), RTX PRO 6000 96GB (~215 tok/s).
What is the maximum context for Flint 40B-A2.5B on MacBook Pro 14/16 M5 Max 128GB?
With an f16 KV cache about 256k tokens, 256k with q8_0 and 256k with q4_0, bounded by the model's own limit.
Related
All models on MacBook Pro 14/16 M5 Max 128GB · Flint 40B-A2.5B on all hardware · VRAM tier · all measured pages · leaderboard
Compare: MacBook Pro 14/16 M5 Max 128GB vs MacBook Pro 14/16 (2024) M4 Max 128GB