Will It Run AI

Can SOLAR 10.7B Instruct v1.0 uncensored run on Intel Arc A770 16GB?

YES — Runs Great

C53Usable
Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored 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
Share:

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 10.7B Instruct v1.0 uncensored 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 10.7B Instruct v1.0 uncensored (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
MediumC50
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 HighC51
F16
16
21.9 GB
MaximumF0

Get started

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 start

Frequently asked questions

Can Intel Arc A770 16GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, Intel Arc A770 16GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 38.6 tok/s.

How much VRAM does SOLAR 10.7B Instruct v1.0 uncensored need?

SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B Instruct v1.0 uncensored?

The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B Instruct v1.0 uncensored run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 38.6 tokens per second decode speed with a time-to-first-token of 5014ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on Intel Arc A770 16GB receives a C grade with 38.6 tok/s and 89K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 89K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if SOLAR 10.7B Instruct v1.0 uncensored feels slow on Intel Arc A770 16GB?

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 A770 16GB for SOLAR 10.7B Instruct v1.0 uncensored?

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.

See all results for Intel Arc A770 16GBSee all hardware for SOLAR 10.7B Instruct v1.0 uncensored
Embed this result▼

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-a770-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: