will it local
The used-market legend: 11GB + 616 GB/s at used-market prices — the fastest pre-3000 Turing card and a real 13B/14B machine GDDR6X-era thermals do NOT apply (this is GDDR6), but early TU102 cards had memory failures — check VRAM temps and warranty void stickers on used units Build llama.cpp with CMAKE_CUDA_ARCHITECTURES=75 or silently lose ~19% to PTX JIT Dual 2080 Ti tensor-parallel runs 27B at ~59.6 tok/s measured — the multi-GPU stack play starts here
computed band 58.0-86.0 tok/s for 8B Q4_K_M (roofline, 616.0 GB/s VRAM); community: 72.9 tok/s Llama 3.1 8B Instruct Q4_K_M (https://www.localscore.ai/accelerator/112); community: 39.5 tok/s Qwen2.5 14B Instruct Q4_K_M (https://www.localscore.ai/accelerator/112); community: 59.6 tok/s Qwen3.6 27B (dual modded 2080 Ti, tensor parallel) Q4 (https://ai-muninn.com/en/blog/qwen38-dual-2080ti-tensor-parallel)
Effective decode window: 0.48–0.72 of 616 GB/s nominal → ~296–444 GB/s effective (llama.cpp decode, Q4_K_M basis; arch window, community-calibrated)
→ 13b-q4 no 14.6 GB vs 11 GB usable — does not fit
→ 3b-q4 full 2.8 GB needed of 11 GB usable — headroom for context
→ 4b-q4 full 3.7 GB needed of 11 GB usable — headroom for context
→ 8b-q4 full 6.1 GB needed of 11 GB usable — headroom for context
→ 72.9 tok/s · Llama 3.1 8B Instruct Q4_K_M LocalScore accelerator/112 · source
→ 39.5 tok/s · Qwen2.5 14B Instruct Q4_K_M LocalScore accelerator/112 · source
→ 59.6 tok/s · Qwen3.6 27B (dual modded 2080 Ti, tensor parallel) Q4 ai-muninn measured (2x GPU) · source
Nominal bandwidth: 616.0 GB/s — see the effective decode window above
→ qwen3:0.6b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 0.6B · 0.6GB · needs ~1GB (4k ctx)
→ qwen3:1.7b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 1.7B · 1.4GB · needs ~1.8GB (4k ctx)
→ qwen3:4b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 4B · 2.6GB · needs ~3.2GB (4k ctx)
→ llama3.2:1b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 1B · 1.3GB · needs ~1.4GB (4k ctx)
→ llama3.2:3b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 3B · 2.0GB · needs ~2.4GB (4k ctx)
→ gemma3:1b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 1B · 1.0GB · needs ~1.1GB (4k ctx)
→ phi4:mini on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) mini 3.8B · 2.5GB · needs ~3GB (4k ctx)
→ moondream:2b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 2B · 1.7GB · needs ~3.7GB (4k ctx)
→ whisper:tiny on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) tiny · 0.1GB · needs ~2.1GB (4k ctx)
→ whisper on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) base · 0.3GB · needs ~2.3GB (4k ctx)
→ whisper:small on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) small · 0.9GB · needs ~2.9GB (4k ctx)
→ whisper:medium on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) medium · 3.1GB · needs ~5.1GB (4k ctx)
→ kokoro:82m on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 82M · 0.3GB · needs ~2.3GB (4k ctx)
→ gemma3:4b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 4B · 3.3GB · needs ~3.8GB (4k ctx)
→ deepseek-r1:7b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) distill 7B · 4.7GB · needs ~4.9GB (4k ctx)
→ mistral:7b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 7B · 4.1GB · needs ~4.6GB (4k ctx)
→ llava:7b on Raspberry Pi 5 + AI Kit (Hailo-8L) ($110) 7B · 4.7GB · needs ~6.7GB (4k ctx)
→ qwen3:8b on Tesla P100 (16GB, used) ($135) 8B · 5.2GB · needs ~5.8GB (4k ctx)
→ qwen2.5vl:7b on Tesla P100 (16GB, used) ($135) 7B · 5.6GB · needs ~5.8GB (4k ctx)
→ llama3.1:8b on Tesla P100 (16GB, used) ($135) 8B · 4.9GB · needs ~5.4GB (4k ctx)
→ deepseek-r1:8b on Tesla P100 (16GB, used) ($135) distill 8B · 4.9GB · needs ~5.4GB (4k ctx)
→ whisper:large-v3 on Tesla P100 (16GB, used) ($135) large-v3 · 6.2GB · needs ~8.2GB (4k ctx)
→ mistral-nemo:12b on Tesla P100 (16GB, used) ($135) 12B · 7.1GB · needs ~7.7GB (4k ctx)
→ sdxl on Tesla P100 (16GB, used) ($135) SDXL base · 6.9GB · needs ~8.9GB (4k ctx)
→ gemma3:12b on Tesla P100 (16GB, used) ($135) 12B · 8.1GB · needs ~9.6GB (4k ctx)
→ qwen3:14b on Tesla P100 (16GB, used) ($135) 14B · 9.3GB · needs ~9.9GB (4k ctx)
→ deepseek-r1:14b on Tesla P100 (16GB, used) ($135) distill 14B · 9.0GB · needs ~9.8GB (4k ctx)
→ phi4:14b on Tesla P100 (16GB, used) ($135) 14B · 9.1GB · needs ~9.9GB (4k ctx)
→ llava:13b 13B — 11.1 of 11 GB usable — barely over; real with q8 KV cache (halves KV) or shorter context
⚠ The used-market legend: 11GB + 616 GB/s at used-market prices — the fastest pre-3000 Turing card and a real 13B/14B machine
⚠ GDDR6X-era thermals do NOT apply (this is GDDR6), but early TU102 cards had memory failures — check VRAM temps and warranty void stickers on used units
⚠ Build llama.cpp with CMAKE_CUDA_ARCHITECTURES=75 or silently lose ~19% to PTX JIT
⚠ Dual 2080 Ti tensor-parallel runs 27B at ~59.6 tok/s measured — the multi-GPU stack play starts here