local intelligence

will it local

Best local LLMs for the NVIDIA GeForce RTX 2060 Super

8GB · Turing TU106

The 2060 family bandwidth trap: base 2060 and 2060 12GB run 192-bit/336 GB/s; the Super runs 256-bit/448 GB/s — do not mix them up when comparing Build llama.cpp with CMAKE_CUDA_ARCHITECTURES=75 or silently lose ~19% to PTX JIT 8GB makes it the Turing pick over the 6GB cards: 8B Q4 fits fully with context room

explore the full catalogupgrade paths with live prices · what fits on each machine

Speed

computed band 42.0-63.0 tok/s for 8B Q4_K_M (roofline, 448.0 GB/s VRAM); community: 52.8-62.5 tok/s 8B Q4 (kb-audit-0903)

Effective decode window: 0.48–0.72 of 448 GB/s nominal → ~215–323 GB/s effective (llama.cpp decode, Q4_K_M basis; arch window, community-calibrated)

What fits (computed)

13b-q4 no 14.6 GB vs 8 GB usable — does not fit

3b-q4 full 2.8 GB needed of 8 GB usable — headroom for context

4b-q4 full 3.7 GB needed of 8 GB usable — headroom for context

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

Cited community benches (1 rows)

→ 52.8-62.5 tok/s · 8B Q4 measured (kb-audit-0903) · kb-audit-0903

Runs fully in memory (machine alternatives)

Nominal bandwidth: 448.0 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)

mistral-nemo:12b on Tesla P100 (16GB, used) ($135) 12B · 7.1GB · needs ~7.7GB (4k ctx)

Borderline — runs, but offloads

→ qwen3:14b 14B — 9.9 GB vs 8 GB usable — partial CPU offload, expect large speed loss

→ gemma3:12b 12B — 9.6 GB vs 8 GB usable — partial CPU offload, expect large speed loss

→ deepseek-r1:14b distill 14B — 9.8 GB vs 8 GB usable — partial CPU offload, expect large speed loss

→ phi4:14b 14B — 9.9 GB vs 8 GB usable — partial CPU offload, expect large speed loss

→ whisper:large-v3 large-v3 — 8.2 of 8 GB usable — barely over; real with q8 KV cache (halves KV) or shorter context

→ sdxl SDXL base — 8.9 GB vs 8 GB usable — partial CPU offload, expect large speed loss

Gotchas

⚠ The 2060 family bandwidth trap: base 2060 and 2060 12GB run 192-bit/336 GB/s; the Super runs 256-bit/448 GB/s — do not mix them up when comparing

⚠ Build llama.cpp with CMAKE_CUDA_ARCHITECTURES=75 or silently lose ~19% to PTX JIT

⚠ 8GB makes it the Turing pick over the 6GB cards: 8B Q4 fits fully with context room

Where to go next

RTX 3060 12GB (used)

$350 — 12GB VRAM — unlocks 13B/14B

eBay (used) ↗ · Amazon ↗

RTX 3080 10GB (used)

$385 — 760 GB/s tier when found cheap

eBay (used) ↗ · Amazon ↗

2026-09-18 · ← full hardware catalog · fit = working set vs VRAM · speeds are community-reported, cited in our knowledge base