stablelm 2 1 6b chat imatrix needs ~6.3 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~56 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
56.1 tok/s
TTFT
3453 ms
Safe context
101K
Memory
6.3 GB / 10.0 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 | 56.1 tok/s | 1884 ms | 101K |
| Coding | C | Runs well | 56.1 tok/s | 3453 ms | 101K |
| Agentic Coding | B | Runs well | 56.1 tok/s | 5023 ms | 101K |
| Reasoning | C | Runs well | 56.1 tok/s | 4081 ms | 101K |
| RAG | B | Runs well | 56.1 tok/s | 6278 ms | 101K |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C50 |
Q3_K_S | 3 | 2.9 GB | Low | C51 |
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
GTX 1080 Ti 11GBBudget pickRaises estimated decode speed by about 39%.
Moves you onto CUDA, which still has the broadest local-AI runtime coverage.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$699 MSRP
RTX 2080 Ti 11GBBest valueRaises estimated decode speed by about 50%.
Moves you onto CUDA, which still has the broadest local-AI runtime coverage.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$999 MSRP
Yes, Intel Arc B570 10GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 56.1 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 6.3 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 B570 10GB, stablelm 2 1 6b chat imatrix achieves approximately 56.1 tokens per second decode speed with a time-to-first-token of 3453ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on Intel Arc B570 10GB receives a C grade with 56.1 tok/s and 101K context.
On Intel Arc B570 10GB, stablelm 2 1 6b chat imatrix can safely use up to 101K 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-b570-10gb" 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 |
| C52 |
Q4_K_M | 4 | 3.7 GB | Medium | C52 |
Q5_K_M | 5 | 4.3 GB | High | C53 |
Q6_K | 6 | 4.9 GB | High | C53 |
Q8_0Best for your GPU | 8 | 6.4 GB | Very High | C52 |
F16 | 16 | 12.3 GB | Maximum | F0 |
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.