SOLAR 10.7B v1.0 needs ~11.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~38 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
37.7 tok/s
TTFT
5132 ms
Safe context
181K
Memory
11.1 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 | 37.7 tok/s | 2799 ms | 181K |
| Coding | C | Runs well | 37.7 tok/s | 5132 ms | 181K |
| Agentic Coding | C | Runs well | 37.7 tok/s | 7464 ms | 181K |
| Reasoning | C | Runs well | 37.7 tok/s | 6065 ms | 181K |
| RAG | C | Runs well | 37.7 tok/s | 9330 ms | 181K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C45 |
Q3_K_S | 3 | 5.2 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run SOLAR 10.7B v1.0 on your machine.
Run
lms load hf-mradermacher--solar-10-7b-v1-0-gguf && lms server startUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 373%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 206%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$2,499 MSRP
Yes, Intel Arc Pro B60 24GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 37.7 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.
The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, SOLAR 10.7B v1.0 achieves approximately 37.7 tokens per second decode speed with a time-to-first-token of 5132ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on Intel Arc Pro B60 24GB receives a C grade with 37.7 tok/s and 181K context.
On Intel Arc Pro B60 24GB, SOLAR 10.7B v1.0 can safely use up to 181K 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-mradermacher--solar-10-7b-v1-0-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:
6.0 GB |
| Medium |
| C46 |
Q4_K_M | 4 | 6.5 GB | Medium | C46 |
Q5_K_M | 5 | 7.7 GB | High | C47 |
Q6_K | 6 | 8.8 GB | High | C47 |
Q8_0Best for your GPU | 8 | 11.4 GB | Very High | C49 |
F16 | 16 | 21.9 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.