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URL: https://willitrunai.com/can-run/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-arc-a770-16gb

⇱ solar finalised finetuned Model 10.7B i1 on Intel Arc A770 …

Can solar finalised finetuned Model 10.7B i1 run on Intel Arc A770 16GB?

YES — Runs Great

C53Usable
○Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~10.3 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.3 GB, 38.6 tok/s, Runs well
10.3 GB required16.0 GB available
64% VRAM used

Fit status

Runs well

Decode

38.6 tok/s

TTFT

5014 ms

Safe context

89K

Memory

10.3 GB / 16.0 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on Intel Arc A770 16GB
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: 38.6 tok/s decode · 5.0s TTFT (warm) · 97 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well38.6 tok/s2735 ms89K
CodingCRuns well38.6 tok/s5014 ms89K
Agentic CodingCRuns well38.6 tok/s7294 ms89K
ReasoningCRuns well38.6 tok/s5926 ms89K
RAGCRuns well38.6 tok/s9117 ms89K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC48
Q3_K_S
3
5.2 GB
LowC49
NVFP4
4
6.0 GB
MediumC49
Q4_K_M
4
6.5 GB
MediumC50
Q5_K_M
5
7.7 GB
HighC51
Q6_K
6
8.8 GB
HighC51
Q8_0Best for your GPU
8
11.4 GB
Very HighC50
F16
16
21.9 GB
MaximumF0

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

Frequently asked questions

See all results for Intel Arc A770 16GBSee all hardware for solar finalised finetuned Model 10.7B i1