Devstral 2 123B Instruct needs ~86.5 GB but MacBook Pro M4 Max 48GB only has 34.6 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
51.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.9 tok/s
TTFT
49422 ms
Safe context
4K
Memory
86.5 GB / 34.6 GB
Offload
60%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 86.5 GB, but this setup only exposes 34.6 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.1 tok/s | 51186 ms | 4K |
| Coding | F | Too heavy | 2.1 tok/s | 93841 ms | 4K |
| Agentic Coding | F | Too heavy | 2.1 tok/s | 136497 ms | 4K |
| Reasoning | F | Too heavy | 2.1 tok/s | 110904 ms | 4K |
| RAG | F | Too heavy | 2.1 tok/s | 170621 ms | 4K |
How Devstral 2 123B Instruct (123B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 48.0 GB | Low | F0 |
Q3_K_S | 3 | 60.3 GB | Low | F0 |
NVFP4 | 4 | 68.9 GB | Medium | F0 |
Q4_K_M | 4 | 75.0 GB | Medium | F0 |
Q5_K_M | 5 | 88.6 GB | High | F0 |
Q6_K | 6 | 100.9 GB | High | F0 |
Q8_0 | 8 | 131.6 GB | Very High | F0 |
F16 | 16 | 252.2 GB | Maximum | F0 |
アップグレードオプション
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$12,000 MSRP
No, Devstral 2 123B Instruct requires more memory than MacBook Pro M4 Max 48GB provides.
Devstral 2 123B Instruct (123B parameters) requires approximately 86.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Devstral 2 123B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 48GB, Devstral 2 123B Instruct achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 93841ms using Q4_K_M quantization.
For coding workloads, Devstral 2 123B Instruct on MacBook Pro M4 Max 48GB receives a F grade with 2.1 tok/s and 4K context.
On MacBook Pro M4 Max 48GB, Devstral 2 123B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M4 Max 48GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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