SOLAR 10.7B v1.0 needs ~9.5 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~20 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
1.5 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
20.2 tok/s
TTFT
9602 ms
Safe context
4K
Memory
9.5 GB / 8.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.6 GB host RAM) | 23.3 tok/s | 4535 ms | 4K |
| Coding | D | Very compromised (needs ~1 GB host RAM) | 20.2 tok/s | 9602 ms | 4K |
| Agentic Coding | F | Too heavy | 15.5 tok/s | 18140 ms | 4K |
| Reasoning | D | Very compromised (needs ~1 GB host RAM) | 20.2 tok/s | 11348 ms | 4K |
| RAG | F | Too heavy | 15.5 tok/s | 22675 ms | 4K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 5.2 GB | Low | C52 |
NVFP4 | 4 | 6.0 GB | Medium | F0 |
Q4_K_M | 4 | 6.5 GB | Medium | F0 |
Q5_K_M | 5 | 7.7 GB | High | F0 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
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 startアップグレードオプション
Intel Arc B570 10GBコスパ重視Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 55%.
〜$219 MSRP
Intel Arc B580 12GBコスパ最良Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 66%.
〜$249 MSRP
Intel Arc A770 16GBIntelアップグレードRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 91%.
〜$349 MSRP
Yes, Intel Arc A580 8GB can run SOLAR 10.7B v1.0 with a D grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 20.2 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 9.5 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 A580 8GB, SOLAR 10.7B v1.0 achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9602ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on Intel Arc A580 8GB receives a D grade with 20.2 tok/s and 4K context.
On Intel Arc A580 8GB, SOLAR 10.7B v1.0 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
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