Can the Intel Arc A770 16GB Run Qwen3 8B?
Runs
Yes. Qwen3 8B at Q4_K_M is a 5 GB download (Hugging Face file listing for unsloth/Qwen3-8B-GGUF), and the Intel Arc A770 16GB has 16 GB of VRAM, leaving about 11 GB for the KV cache and runtime. Published runs put generation at a median of 21 tokens per second across 3 cited measurements. Fastest cited run: diunox/arc-inference-bench (GitHub).
Buy the Intel Arc A770 16GB
ASRock Intel Arc A770 Phantom Gaming 16GB OC Graphics Card, 2200 MHz GPU Clock, 16GB…Tracked at $300 on 2026-09-23 — price may vary.
Published runs for Qwen3 8B on the Intel Arc A770 16GB
| Generation | Quant | Runtime | Context | Source | Date |
|---|---|---|---|---|---|
| 45.5 tok/s | INT4 | openvino | — | diunox/arc-inference-bench (GitHub)labelled "qwen3:8b" | 2026-03-16 |
| 21 tok/s | q4_K_M | llama.cpp | — | diunox/arc-inference-bench (GitHub)labelled "qwen3:8b" | 2026-03-15 |
| 18 tok/s | not stated | ollama | — | LocalLLaMAlabelled "qwen3:8b" | 2026-04-14 |
More about this pair
How much VRAM does Qwen3 8B need?
The Q4_K_M file is 5 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 6.5 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What is the fastest Qwen3 8B can generate on the Intel Arc A770 16GB?
Generation reads every weight once per token, so the Intel Arc A770 16GB's 560 GB/s of memory bandwidth divided by the 5 GB file sets a ceiling near 111 tokens per second. Real runs land below it; SpecPicks rejects any published figure above it.
Check another pair
See the full matrix → · All Intel Arc A770 16GB benchmarks →
Qwen3 8B on other GPUs
- RTX 4060 — Runs
- RTX 3060 Ti — Runs
- RTX 3060 12GB — Runs, 42 tok/s median
- Intel Arc B580 — Runs, 24.2 tok/s median
- RTX 4070 SUPER — Runs, 65.8 tok/s median
- RTX 4060 Ti 16GB — Runs, 34.3 tok/s median
- RTX 5060 Ti 16GB — Runs, 51.2 tok/s median
- Radeon RX 9070 XT — Runs, 90 tok/s median
- RTX 4070 Ti SUPER — Runs, 63.4 tok/s median
- RTX 4080 — Runs, 77.9 tok/s median
- RTX 5070 Ti — Runs, 121 tok/s median
- RTX 5080 — Runs, 94.1 tok/s median
- RTX 3090 — Runs, 101 tok/s median
- Radeon RX 7900 XTX — Runs
- RTX 4090 — Runs
- RTX 5090 — Runs, 186 tok/s median
- RTX A6000 48GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs
Other models on the Intel Arc A770 16GB
- Llama 3.2 1B — Runs
- Llama 3.2 3B — Runs, 64.2 tok/s median
- Mistral 7B — Runs, 41.5 tok/s median
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs, 38.7 tok/s median
- DeepSeek-R1-Distill-Llama 8B — Runs
- Gemma 2 9B — Runs, 23.6 tok/s median
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 21.4 tok/s median
- DeepSeek-R1-Distill-Qwen 14B — Runs, 13.4 tok/s median
- Qwen3 14B — Runs, 23.2 tok/s median
- Phi-4 14B — Runs, 22.3 tok/s median
- gpt-oss-20b — Runs
- Gemma 3 27B — Offload
- Gemma 2 27B — Offload
- Qwen3 30B-A3B — Offload
- Qwen3 32B — Offload
- Qwen2.5 32B — Offload
- DeepSeek-R1-Distill-Qwen 32B — Offload
- Llama 3.1 70B — Offload
- Llama 3.3 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
How the checks work
The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling above, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
As an Amazon Associate, SpecPicks earns from qualifying purchases.
This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.