stablelm 2 1 6b chat imatrix needs ~12.2 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~66 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
65.6 tok/s
TTFT
2952 ms
Safe context
788K
Memory
12.2 GB / 46.1 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 | 65.6 tok/s | 1610 ms | 788K |
| Coding | C | Runs well | 65.6 tok/s | 2952 ms | 788K |
| Agentic Coding | C | Runs well | 65.6 tok/s | 4294 ms | 788K |
| Reasoning | C | Runs well | 65.6 tok/s | 3489 ms | 788K |
| RAG | C | Runs well | 65.6 tok/s | 5368 ms | 788K |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C41 |
Q3_K_S | 3 | 2.9 GB | Low | C41 |
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 startYes, MacBook Pro M3 Max 64GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 65.6 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 12.2 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 M3 Max 64GB, stablelm 2 1 6b chat imatrix achieves approximately 65.6 tokens per second decode speed with a time-to-first-token of 2952ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on MacBook Pro M3 Max 64GB receives a C grade with 65.6 tok/s and 788K context.
On MacBook Pro M3 Max 64GB, stablelm 2 1 6b chat imatrix can safely use up to 788K 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-m3-max-64gb" 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 |
| C41 |
Q4_K_M | 4 | 3.7 GB | Medium | C41 |
Q5_K_M | 5 | 4.3 GB | High | C42 |
Q6_K | 6 | 4.9 GB | High | C42 |
Q8_0 | 8 | 6.4 GB | Very High | C42 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C44 |
Not always. MacBook Pro M3 Max 64GB 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.