IntelAlchemist

Best AI Models for Intel Arc A770 16GB (16.0GB)

VRAM:16.0 GB GDDR6·Bandwidth:560.0 GB/s·TDP:225W·MSRP:$349

16 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.

This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.

Runs Well

  • 7B models at Q4–Q6 quality with good speed
  • Small models (3B–4B) at Q8 or FP16
  • 9B models (Gemma 2 9B) at Q4_K_M

Challenging

  • 13B–14B models need Q3 or lower
  • 30B+ models do not fit in VRAM
  • Long context (>8K tokens) with larger models

What LLMs Can Intel Arc A770 16GB Run?

110 models · 26 excellent · 46 good

Showing compatibility for Intel Arc A770 16GB

LLM models compatible with Intel Arc A770 16GB — ranked by performance
ModelVRAMGrade
Qwen1.5 14B14.2B
Q4_K_M·26.7 t/s tok/s·33K ctx·RUNS WELL
10.5 GBA68
Phi 414.7B
Q4_K_M·29.4 t/s tok/s·16K ctx·RUNS WELL
9.5 GBA70
GPT OSS 20B21.5B
Q4_K_M·21.1 t/s tok/s·131K ctx·DECENT
13.3 GBB57
Q3_K_M·21.1 t/s tok/s·262K ctx·DECENT
13.2 GBB57
Q4_K_M·34.0 t/s tok/s·262K ctx·RUNS WELL
8.2 GBA73
Phi 4 Reasoning14.7B
Q4_K_M·29.4 t/s tok/s·33K ctx·RUNS WELL
9.5 GBA70
Gemma 3 12B IT12.2B
Q4_K_M·34.8 t/s tok/s·33K ctx·RUNS WELL
8.0 GBA73
Q4_K_M·32.6 t/s tok/s·RUNS WELL
8.6 GBA72
Gemma 4 12B12.0B
Q4_K_M·34.0 t/s tok/s·262K ctx·RUNS WELL
8.2 GBA73
Q4_K_M·34.7 t/s tok/s·131K ctx·RUNS WELL
8.1 GBA73
Qwen 14B Chat14.2B
Q4_K_M·29.9 t/s tok/s·8K ctx·RUNS WELL
9.3 GBA71
Qwen3 8B8.2B
Q4_K_M·50.7 t/s tok/s·41K ctx·RUNS WELL
5.5 GBA80
Q4_K_M·32.6 t/s tok/s·RUNS WELL
8.6 GBA72
Qwen 14B14.2B
Q4_K_M·29.9 t/s tok/s·8K ctx·RUNS WELL
9.3 GBA71
Q4_K_M·32.6 t/s tok/s·RUNS WELL
8.6 GBA72
Q4_K_M·45.1 t/s tok/s·262K ctx·RUNS WELL
6.2 GBA77

Intel Arc A770 16GB Specifications

Brand
Intel
Architecture
Alchemist
VRAM
16.0 GB GDDR6
Memory Bandwidth
560.0 GB/s
FP16 Performance
39.30 TFLOPS
TDP
225W
Release Date
2022-10-12
MSRP
$349

Get Started

Ollama (Recommended)

$curl -fsSL https://ollama.com/install.sh | sh
$ollama run llama3:8b

LM Studio

LM Studio

Download LM Studio, search for a model, and run it with one click.

Prompt Processing

Estimated for MiniCPM MoE 8x2B, the compute-bound phase that reads your prompt before the first reply token appears.

2,563tok/s prefill

Short chat

200 ms

512 tok prompt

Long chat

1.6 s

4,096 tok prompt

Document / codebase

12.8 s

32,768 tok prompt

Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →

Efficiency & Value

Based on MiniCPM MoE 8x2B at ~26.3 tok/s decode.

Tokens per watt

0.12tok/s per W

Higher is better.

$ per tok/s (MSRP)

$13.27

MSRP-based, not street price. Lower is better.

How efficiency & value are calculated →

Performance figures are estimates calibrated as of 2026-07-30 see calibration basis →

GPUs to Consider Over Intel Arc A770 16GB

Similar GPUs and upgrades with more VRAM or higher bandwidth for AI

Frequently Asked Questions

Can Intel Arc A770 16GB run Qwen1.5 14B?

Yes, the Intel Arc A770 16GB with 16 GB can run Qwen1.5 14B, Phi 4, GPT OSS 20B, and 1440 other models. 548 models run at excellent quality, and 619 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.

Is Intel Arc A770 16GB good for AI?

The Intel Arc A770 16GB has 16 GB of GDDR6, making it very good for running local AI models. It supports 1167 models at good quality or better. With 560.0 GB/s memory bandwidth, it delivers solid token generation speeds. This is a solid mid-range card for running 7B–14B parameter models at good quality.

How many parameters can Intel Arc A770 16GB handle?

With 16 GB, the Intel Arc A770 16GB supports models from 3B to 14B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 26B parameters. 7B models run at high quality (Q5/Q6), while 14B models fit comfortably at Q4.

What quantization should I use on Intel Arc A770 16GB?

For the best balance of quality and speed on the Intel Arc A770 16GB, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. You can step up to Q5_K_M for 7B models to get better quality. For 14B models that just barely fit, Q4_K_M is ideal.

How fast is Intel Arc A770 16GB for AI inference?

With 560.0 GB/s memory bandwidth, the Intel Arc A770 16GB achieves approximately 62 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~31 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.

tok/s = (560 GB/s ÷ model GB) × efficiency

Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.

Estimated speed on Intel Arc A770 16GB

~27 tok/s
~29 tok/s
~21 tok/s

Real-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.

Learn more about tok/s estimation →

What's the best model for Intel Arc A770 16GB?

The top-rated models for the Intel Arc A770 16GB are Qwen1.5 14B, Phi 4, GPT OSS 20B. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.

What power supply and cooling does Intel Arc A770 16GB need?

The Intel Arc A770 16GB has a TDP of 225 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 550 W PSU or larger. A mid-tower case with one intake and one rear exhaust is usually sufficient. Keep dust filters clean, as sustained inference generates continuous heat rather than the brief spikes typical of gaming.