Can I Run Llama 3.3 on Intel Arc B570?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 12 lipca 2026
No — Llama 3.3 70B Instruct at Q2_K_XS (Tight) needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), and the Intel Arc B570's 10 GB leaves it ~13.7 GB short. The cheapest card that runs it is the NVIDIA GeForce RTX 5090 Laptop GPU (24 GB).
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Intel Arc B570 Specs
| VRAM | 10 GB |
| Memory Bandwidth | 380 GB/s |
Llama 3.3 70B Instruct on the Intel Arc B570: VRAM by quantization
| Quant | VRAM needed | Fits 10 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 143.5 GB | ✗ No | — | — |
| Q8_0 | 77.9 GB | ✗ No | — | — |
| Q6_K | 60.9 GB | ✗ No | — | — |
| Q5_K_M | 53.1 GB | ✗ No | — | — |
| Q4_K_M | 45.7 GB | ✗ No | — | — |
| Q3_K_M | 33.3 GB | ✗ No | — | — |
| Q2_K | 26.5 GB | ✗ No | — | — |
Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 10 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q2_K_XS (Tight) on the Intel Arc B570: 8K 23.7 GB ✗ · 32K 31.7 GB ✗ · 128K 64 GB ✗. Past 8K it no longer fits 10 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? Llama 3.3 70B Instruct on 256 GB of system RAM, CPU only: runs, but too slowly to be worth it.
Why It Does Not Fit
Every Llama 3.3 variant requires more VRAM than the Intel Arc B570 provides (10 GB).
Nearest GPU That Fits
NVIDIA GeForce RTX 5090 Laptop GPU (24 GB VRAM).
Llama 3.3 needs ~24 GB but this GPU has 10 GB. Rent a RTX 4090 (24 GB)-class GPU by the hour instead of buying one:
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FAQ
Is the Intel Arc B570 enough to run Llama 3.3 locally?
No — Llama 3.3 70B Instruct at Q2_K_XS (Tight) needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), and the Intel Arc B570's 10 GB leaves it ~13.7 GB short. The cheapest card that runs it is the NVIDIA GeForce RTX 5090 Laptop GPU (24 GB).
What's the cheapest GPU that runs Llama 3.3?
The NVIDIA GeForce RTX 5090 Laptop GPU (24 GB VRAM) is the cheapest upgrade that fits it.
Llama 3.3 on Other GPUs
- Llama 3.3 on NVIDIA GeForce RTX 3080 (10GB)
- Llama 3.3 on NVIDIA GeForce RTX 5070
- Llama 3.3 on NVIDIA GeForce RTX 4070 Ti
Popular Models on the Intel Arc B570
VRAM Tier
Troubleshooting
- Ollama: "model requires more system memory than is available"
- Which GGUF quant should I download? (Q4 vs Q5 vs Q8)
Buying Guide
← Can I Run It? | Llama 3.3 Model Page | Intel Arc B570 GPU Page | VRAM calculator | Check Your Hardware