stablelm 2 1 6b chat imatrix needs ~7.9 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~18 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
17.8 tok/s
TTFT
10901 ms
Safe context
230K
Memory
7.9 GB / 17.3 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 | 17.8 tok/s | 5946 ms | 230K |
| Coding | C | Runs well | 17.8 tok/s | 10901 ms | 230K |
| Agentic Coding | C | Runs well | 17.8 tok/s | 15856 ms | 230K |
| Reasoning | C | Runs well | 17.8 tok/s | 12883 ms | 230K |
| RAG | C | Runs well | 17.8 tok/s | 19820 ms | 230K |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C46 |
Q3_K_S | 3 | 2.9 GB | Low | C46 |
NVFP4 | 4 | 3.4 GB | Medium | C47 |
Q4_K_M | 4 | 3.7 GB | Medium | C47 |
Q5_K_M | 5 | 4.3 GB | High | C47 |
Q6_K | 6 | 4.9 GB | High | C48 |
Q8_0 | 8 | 6.4 GB | Very High | C49 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
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 startアップグレードオプション
Raises estimated decode speed by about 256%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Raises estimated decode speed by about 115%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Raises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Yes, Mac mini M2 24GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 17.8 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 7.9 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 Mac mini M2 24GB, stablelm 2 1 6b chat imatrix achieves approximately 17.8 tokens per second decode speed with a time-to-first-token of 10901ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on Mac mini M2 24GB receives a C grade with 17.8 tok/s and 230K context.
On Mac mini M2 24GB, stablelm 2 1 6b chat imatrix can safely use up to 230K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac mini M2 24GB 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.
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-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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