RX 7600 XT 16GBBest value
16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
SOLAR 10.7B Instruct v1.0 uncensored needs ~9.9 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~34 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
Tight fit
Decode
33.5 tok/s
TTFT
5773 ms
Safe context
43K
Memory
9.9 GB / 12.0 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 | 33.5 tok/s | 3149 ms | 43K |
| Coding | C | Tight fit | 33.5 tok/s | 5773 ms | 43K |
| Agentic Coding | C | Tight fit | 33.5 tok/s | 8397 ms | 43K |
| Reasoning | C | Tight fit | 33.5 tok/s | 6823 ms | 43K |
| RAG | C | Tight fit | 33.5 tok/s | 10497 ms | 43K |
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C51 |
Q3_K_S | 3 | 5.2 GB | Low | C52 |
NVFP4 | 4 |
Copy-paste commands to run SOLAR 10.7B Instruct v1.0 uncensored on your machine.
Run
lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Intel Arc A770 16GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$349 MSRP
Yes, Intel Arc B580 12GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Tight fit). Expected decode speed: 33.5 tok/s.
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B580 12GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 33.5 tokens per second decode speed with a time-to-first-token of 5773ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on Intel Arc B580 12GB receives a C grade with 33.5 tok/s and 43K context.
On Intel Arc B580 12GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 43K 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-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf-on-arc-b580-12gb" 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 |
| C52 |
Q4_K_M | 4 | 6.5 GB | Medium | C52 |
Q5_K_M | 5 | 7.7 GB | High | C52 |
Q6_KBest for your GPU | 6 | 8.8 GB | High | C51 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
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.