MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)
C
This setup is broadly balanced for this model.76.9 tok/s decode
~$2,499 MSRP
stablelm 2 1 6b chat imatrix needs ~7.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~67 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
67.3 tok/s
TTFT
2878 ms
Safe context
388K
Memory
7.7 GB / 24.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 67.3 tok/s | 1570 ms | 388K |
| Coding | C | Runs well | 67.3 tok/s | 2878 ms | 388K |
| Agentic Coding | C | Runs well | 67.3 tok/s | 4186 ms | 388K |
| Reasoning | C | Runs well | 67.3 tok/s | 3401 ms | 388K |
| RAG | C | Runs well | 67.3 tok/s | 5232 ms | 388K |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 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
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, Intel Arc Pro B60 24GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 67.3 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 7.7 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 Intel Arc Pro B60 24GB, stablelm 2 1 6b chat imatrix achieves approximately 67.3 tokens per second decode speed with a time-to-first-token of 2878ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on Intel Arc Pro B60 24GB receives a C grade with 67.3 tok/s and 388K context.
On Intel Arc Pro B60 24GB, stablelm 2 1 6b chat imatrix can safely use up to 388K 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-arc-pro-b60-24gb" 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 |
| C44 |
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 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.