Intel Arc A770 16GBコスパ重視16 GB VRAM (+4)560 GB/s (+176)
C
Raises estimated decode speed by about 34%.38.6 tok/sデコード
Raises estimated decode speed by about 34%.
Adds memory headroom for longer context windows and future model growth.
〜$349 MSRP
Can solar finalised finetuned Model 10.7B i1 run on Intel Arc Pro A60 12GB?
YES — Tight Fit
C50Usable
Estimated from fit model
solar finalised finetuned Model 10.7B i1 needs ~9.9 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~29 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 9.9 GB, 28.8 tok/s, Tight fit
9.9 GB required12.0 GB available
83% VRAM used
Fit status
Tight fit
Decode
28.8 tok/s
TTFT
6716 ms
Safe context
43K
Memory
9.9 GB / 12.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelssolar finalised finetuned Model 10.7B i1 on Intel Arc Pro A60 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 28.8 tok/s decode · 6.7s TTFT (warm) · 72 tok/s prefill
What limits this setup
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.
Best improvement path
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.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 28.8 tok/s | 3663 ms | 43K |
| Coding | C | Tight fit | 28.8 tok/s | 6716 ms | 43K |
| Agentic Coding | C | Tight fit | 28.8 tok/s | 9768 ms | 43K |
| Reasoning | C | Tight fit | 28.8 tok/s | 7937 ms | 43K |
| RAG | C | Tight fit | 28.8 tok/s | 12210 ms | 43K |
Quantization options
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on Intel Arc Pro A60 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 | 6.0 GB | Medium | C52 |
Q4_K_M | 4 | 6.5 GB | Medium | C52 |
Q5_K_M | 5 | 7.7 GB | High | C51 |
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 |
Get started
Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.
Run
lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server startアップグレードオプション
solar finalised finetuned Model 10.7B i1を快適に動かすハードウェア
Frequently asked questions
Can Intel Arc Pro A60 12GB run solar finalised finetuned Model 10.7B i1?
Yes, Intel Arc Pro A60 12GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 28.8 tok/s.
How much VRAM does solar finalised finetuned Model 10.7B i1 need?
solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
What is the best quantization for solar finalised finetuned Model 10.7B i1?
The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will solar finalised finetuned Model 10.7B i1 run at on Intel Arc Pro A60 12GB?
On Intel Arc Pro A60 12GB, solar finalised finetuned Model 10.7B i1 achieves approximately 28.8 tokens per second decode speed with a time-to-first-token of 6716ms using Q4_K_M quantization.
Can Intel Arc Pro A60 12GB run solar finalised finetuned Model 10.7B i1 for coding?
For coding workloads, solar finalised finetuned Model 10.7B i1 on Intel Arc Pro A60 12GB receives a C grade with 28.8 tok/s and 43K context.
What context window can solar finalised finetuned Model 10.7B i1 use on Intel Arc Pro A60 12GB?
On Intel Arc Pro A60 12GB, solar finalised finetuned Model 10.7B i1 can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if solar finalised finetuned Model 10.7B i1 feels slow on Intel Arc Pro A60 12GB?
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.
Would CUDA be a better path than Intel Arc Pro A60 12GB for solar finalised finetuned Model 10.7B i1?
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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