will it local

Best local LLMs for the NVIDIA GeForce RTX 5070 Ti

Speed

computed band 100.0-140.0 tok/s for 8B Q4_K_M (roofline, 896.0 GB/s VRAM); community: 125 tok/s Meta Llama 3.1 8B Instruct Q4_K_M (https://www.kunalganglani.com/llm-benchmarks); community: 105 tok/s Llama 3 8B Q4 (https://www.compute-market.com/blog/rtx-5070-ti-local-ai-2026)

Effective decode window: 0.60–0.80 of 896 GB/s nominal → ~538–717 GB/s effective (llama.cpp decode, Q4_K_M basis; card-specific window from cited community benches)

What fits (computed)

→ 13b-q4 tight 14.6 of 16 GB usable — keep context modest

→ 3b-q4 full 2.8 GB needed of 16 GB usable — headroom for context

→ 4b-q4 full 3.7 GB needed of 16 GB usable — headroom for context

→ 8b-q4 full 6.1 GB needed of 16 GB usable — headroom for context

Cited community benches (2 rows)

→ 125 tok/s · Meta Llama 3.1 8B Instruct Q4_K_M Kunal Ganglani bench DB (reference, llama.cpp) · source

→ 105 tok/s · Llama 3 8B Q4 Compute Market 2026 roundup (90-120 range) · source

Runs fully in memory (machine alternatives)

Nominal bandwidth: 896.0 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)

→ llava:13b on Tesla P100 (16GB, used) ($135) 13B · 8.0GB · needs ~11.1GB (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)

→ gpt-oss:20b on Tesla P40 (24GB, used) ($290) 20B MoE · 14GB · needs ~14.2GB (4k ctx)

→ devstral:24b on Tesla P40 (24GB, used) ($290) 24B · 14.6GB · needs ~15.2GB (4k ctx)

Borderline — runs, but offloads

→ qwen3.8:27b 27B — 18.5 GB vs 16 GB usable — partial CPU offload, expect large speed loss

→ qwen3:30b-a3b 30B-A3B MoE — 19.0 GB vs 16 GB usable — partial CPU offload, expect large speed loss

→ qwen3-coder:30b-a3b 30B-A3B MoE — 19.0 GB vs 16 GB usable — partial CPU offload, expect large speed loss

→ gemma3:27b 27B — 18.9 GB vs 16 GB usable — partial CPU offload, expect large speed loss

→ flux:schnell schnell fp8 — 19.0 GB vs 16 GB usable — partial CPU offload, expect large speed loss

Gotchas

⚠ THE 16GB Blackwell value play: 896 GB/s at its $749 MSRP — the 5080's ~8% decode edge costs ~$250 more at MSRP (more on the street)

⚠ 16GB wall: 14B Q4 comfortable, 32B Q4 does not fit

⚠ Community 8B rows (87-125 tok/s) spread wider than the arch window — band widened via eff_override

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