MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+10)
C
This setup is broadly balanced for this model.76.9 tok/s decode
~$2,499 MSRP
stablelm 2 1 6b chat imatrix needs ~8.7 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~63 tok/s.
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
Fit status
Runs well
Decode
63.4 tok/s
TTFT
3054 ms
Safe context
342K
Memory
8.7 GB / 23.0 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 63.4 tok/s | 1666 ms | 342K |
| Coding | C | Runs well | 63.4 tok/s | 3054 ms | 342K |
| Agentic Coding | C | Runs well | 63.4 tok/s | 4442 ms | 342K |
| Reasoning | C | Runs well | 63.4 tok/s | 3610 ms | 342K |
| RAG | C | Runs well | 63.4 tok/s | 5553 ms | 342K |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run stablelm 2 1 6b chat imatrix on your machine.
Run
lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server startUpgrade options
Yes, MacBook Pro M2 Max 32GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 63.4 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 32GB, stablelm 2 1 6b chat imatrix achieves approximately 63.4 tokens per second decode speed with a time-to-first-token of 3054ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on MacBook Pro M2 Max 32GB receives a C grade with 63.4 tok/s and 342K context.
On MacBook Pro M2 Max 32GB, stablelm 2 1 6b chat imatrix can safely use up to 342K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf-on-m2-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.4 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 3.7 GB | Medium | C45 |
Q5_K_M | 5 | 4.3 GB | High | C45 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Not always. MacBook Pro M2 Max 32GB 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.