Arc A770 16G — what LLMs can it run?
51 of 89 indexed models fit comfortably in 16GB at 4K context, each at the highest-quality quant that still leaves headroom.
The short answer for Arc A770 16G
16 GB, 560 GB/s GDDR6. 51 of 89 models in this index fit comfortably at 4K context; 58 load at all.
- Biggest that fits
- ERNIE 4.5 21B-A3B — 14.1 GB at Q4_K_M — 1.9 GB spare at 4K, so longer context comes out of a thin margin.
- Room to grow
- Stable LM 2 12B Chat — 8.3 GB at Q4_K_M — under 60% of the card, which leaves 7.7 GB for a long context window or a second process.
- Speed ceiling
- Llama 3.1 8B Instruct at Q4_K_M reads 4.6 GB of weights per token, so 560 GB/s puts a hard ceiling near 121 tok/s. That is arithmetic on two published numbers, not a benchmark — real throughput lands below it. No run on this card has been measured here, so there is nothing to compare the ceiling against.
- The wall you will hit
- Backend coverage, not capacity. At 560 GB/s the card has the memory system for its size class; what limits it is which runtimes reach it. llama.cpp's SYCL backend lists Arc A-series and B580 cards as verified, and Ollama runs them through Vulkan — so GGUF is the format this site recommends here. AWQ, GPTQ and EXL2 are not.
32B · 2
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| ERNIE 4.5 21B-A3B21B-A3B | Q4_K_M | 14.05 GB | +1.9 GB | — |
| GPT-OSS 20B21B MoE | MXFP4 | 12.76 GB | +3.2 GB | 195Community |
14B · 16
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| InternLM2 20B Chat20B | Q4_K_M | 13.43 GB | +2.6 GB | 78Estimated |
| DeepSeek-Coder-V2-Lite Instruct16B-A2.4B | Q4_K_M | 10.08 GB | +5.9 GB | 145Estimated |
| DeepSeek-V2-Lite Chat16B-A2.4B | Q4_K_M | 10.08 GB | +5.9 GB | 142Estimated |
| StarCoder2 15B15B | Q4_K_M | 10.19 GB | +5.8 GB | 92Estimated |
| Qwen3 14B Instruct14B | Q5_K_M | 11.65 GB | +4.4 GB | 78 |
| Qwen2.5 14B Instruct14B | Q5_K_M | 11.73 GB | +4.3 GB | 86Estimated |
| DeepSeek-R1-Distill-Qwen-14B14B | Q4_K_M | 10.14 GB | +5.9 GB | 95 |
| Phi-4 14B14B | Q5_K_M | 11.77 GB | +4.2 GB | 78Estimated |
| Phi-3 Medium 14B Instruct14B | Q6_K | 12.86 GB | +3.1 GB | 88Estimated |
| Mistral Nemo 12B Instruct12B | Q6_K | 11.14 GB | +4.9 GB | 95Estimated |
| Gemma 3 12B IT12B | Q5_K_M | 9.84 GB | +6.2 GB | 92Estimated |
| Stable LM 2 12B Chat12B | Q4_K_M | 8.35 GB | +7.7 GB | 108Estimated |
| Llama 3.2 11B Vision Instruct11B | Q8_0 | 12.9 GB | +3.1 GB | 72Estimated |
| Solar 10.7B Instruct11B | Q4_K_M | 7.6 GB | +8.4 GB | 125Estimated |
| Falcon 3 10B Instruct10B | Q4_K_M | 7.28 GB | +8.7 GB | 118Estimated |
| Gemma 2 9B Instruct9B | Q8_0 | 11.7 GB | +4.3 GB | 108Estimated |
7B · 22
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| GLM-4-9B-Chat9B | Q8_0 | 10.16 GB | +5.8 GB | 105Estimated |
| Qwen3-VL 8B Instruct8B | Q8_0 | 10.39 GB | +5.6 GB | 108Estimated |
| Ministral 3 8B Instruct8B | Q8_0 | 10.02 GB | +6 GB | — |
| Qwen2-VL 7B Instruct7B | Q4_K_M | 5.49 GB | +10.5 GB | 72Estimated |
| Granite 3.1 8B Instruct8B | Q4_K_M | 5.88 GB | +10.1 GB | 142Estimated |
| Qwen3 8B Instruct8B | Q6_K | 7.64 GB | +8.4 GB | 122 |
| Llama 3.1 8B Instruct8B | Q8_0 | 9.47 GB | +6.5 GB | 118 |
| Nous Hermes 3 Llama 3.1 8B8B | Q4_K_M | 5.64 GB | +10.4 GB | 148Estimated |
| Aya 23 8B8B | Q4_K_M | 5.64 GB | +10.4 GB | 145Estimated |
| OpenChat 3.6 8B8B | Q4_K_M | 5.64 GB | +10.4 GB | 146Estimated |
| DeepSeek-R1-Distill-Llama-8B8B | Q5_K_M | 6.51 GB | +9.5 GB | 128Estimated |
| InternLM2 7B Chat7B | Q4_K_M | 5.45 GB | +10.6 GB | 148Estimated |
| Qwen2.5 7B Instruct7B | Q6_K | 6.77 GB | +9.2 GB | 132 |
| Qwen2.5-Coder 7B Instruct7B | Q4_K_M | 5.07 GB | +10.9 GB | 158Estimated |
| DeepSeek-R1-Distill-Qwen-7B7B | Q4_K_M | 5.07 GB | +10.9 GB | 152Estimated |
| Gemma 4 E4B ITE4B | Q8_0 | 8.46 GB | +7.5 GB | — |
| OLMo 2 7B Instruct7B | Q8_0 | 10.3 GB | +5.7 GB | 125Estimated |
| WizardLM-2 7B7B | Q4_K_M | 5.14 GB | +10.9 GB | 152Estimated |
| Mistral 7B Instruct v0.37B | Q6_K | 6.75 GB | +9.3 GB | 135Estimated |
| Zephyr 7B Beta7B | Q6_K | 6.75 GB | +9.3 GB | 132Estimated |
| Gemma 4 E2B ITE2B | Q8_0 | 5.2 GB | +10.8 GB | — |
| Gemma 3 4B IT4B | Q8_0 | 5.05 GB | +11 GB | 145Estimated |
≤3B · 11
| Model | Quant | Est. VRAM | Headroom | tok/s on RTX 4090 |
|---|---|---|---|---|
| Qwen3 4B Instruct4B | Q6_K | 4.05 GB | +12 GB | 145 |
| Phi-4 Mini Instruct3.8B | Q8_0 | 4.81 GB | +11.2 GB | 262Estimated |
| Phi-3.5 Mini Instruct3.8B | Q8_0 | 5.89 GB | +10.1 GB | 255Estimated |
| Llama 3.2 3B Instruct3B | Q8_0 | 4.05 GB | +12 GB | 285Estimated |
| Qwen2.5 3B Instruct3B | Q8_0 | 3.59 GB | +12.4 GB | 290Estimated |
| SmolLM3 3B3B | Q8_0 | 3.73 GB | +12.3 GB | — |
| Gemma 2 2B Instruct2B | Q8_0 | 3.34 GB | +12.7 GB | 320Estimated |
| Qwen3 1.7B Instruct1.7B | Q8_0 | 2.39 GB | +13.6 GB | 240Estimated |
| Qwen2.5 1.5B Instruct1.5B | Q8_0 | 1.83 GB | +14.2 GB | 410Estimated |
| Llama 3.2 1B Instruct1B | Q8_0 | 1.51 GB | +14.5 GB | 450Estimated |
| Qwen2.5 0.5B Instruct0.5B | Q8_0 | 0.6 GB | +15.4 GB | 540Estimated |
How this list is built
Each row is the lowest-perplexity-loss quant of that model whose estimated total — weights plus KV cache at 4K context plus activation buffer — uses at most 88% of the card. That is the calculator's "green" threshold, so every row here has real headroom rather than only just fitting. Raise the context length and the list shortens; the calculator lets you check any combination directly.
A further 7 models load but with no headroom to spare (up to 105% of VRAM) — the Quant Hub’s GPU chips count those too, which is why its number is higher.
Measured on this card
No benchmark runs in this index were recorded on a Arc A770 16G. Every figure on this page is calculated from the model architecture and the quant level — treat them as estimates, not measurements. The speed column in the tables above is an RTX 4090 figure shown for reference — it is not a speed on this card.
Cards with the same budget
The same 16GB in system memory is a different machine — everything on this page fits there, at a speed set by your DIMMs rather than a graphics bus: 16 GB RAM (CPU)
Common questions
What is the best local LLM for an Arc A770 16G?
For everyday use, Stable LM 2 12B Chat at Q4_K_M — about 8.3 GB of the card's 16 GB at 4K context, leaving 7.7 GB for a longer window. If you want the largest thing that will load, that is ERNIE 4.5 21B-A3B at Q4_K_M (14.1 GB, 1.9 GB spare). "Best" here means best fit for the memory budget — this index does not run task benchmarks, so it cannot tell you which model is smarter.
Can an Arc A770 16G run Codestral 22B?
Only just. Its smallest build here, Q4_K_M, needs about 15.0 GB against 16.0 GB usable — that loads with nothing else running, with no margin for a longer context window. It is not on the list above, which requires a model to stay inside 88% of usable capacity.
How many tokens per second does an Arc A770 16G do on an 8B model at Q4?
This index has no measured run on this card, so it does not publish a figure. What can be stated from specifications: generating a token requires reading every weight once, Llama 3.1 8B Instruct at Q4_K_M is 4.6 GB of weights, and this card moves 560 GB/s — a ceiling near 121 tok/s. Batch size, context length, the runtime and how much of the model sits in cache all take you below it.
Arc A770 16G or RTX 5080 for local LLMs?
They hold the same models: 16 GB against 16 GB fits 51 and 55 of 89 respectively at 4K. The difference is throughput — 560 GB/s against 960 GB/s, a 1.7× gap in how fast the weights can be read, which is what token generation is bound by. The RTX 5080 generates faster on any model both can hold.
51 of 89 indexed models fit comfortably in 16GB at 4K context, each at the highest-quality quant that still leaves headroom.
This page's figures change when the model or the runtime does.Last updated 2026-10-03 RSS → /feed.xml