will it local
Best price-to-VRAM in the legacy stack on the used market. An 11GB card that genuinely runs 13B Q4 is a different species from any 8GB card. Pascal FP16 penalty applies in full; 250W card - check used PSU.
computed band 26.0-47.0 tok/s for 8B Q4_K_M (roofline, 484 GB/s VRAM); community: 62 tok/s 7B Q4_0 (kb-audit-0903); community: 13-16 tok/s 8B Q4_K_M (kb-audit-0903); community: 8-12 tok/s 13B Q4 (kb-audit-0903)
Effective decode window: 0.28–0.50 of 484 GB/s nominal → ~136–242 GB/s effective (llama.cpp decode, Q4_K_M basis; arch window, community-calibrated)
Community check — localmaxxing.com medians (2 buckets): localmaxxing.com
→ ~53 tok/s · 8-9B Q4 · 5 runs · just above our band (12% over the high edge)
→ ~38 tok/s · 8-9B Q5 · 5 runs · inside our computed band
→ 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
→ 62 tok/s · 7B Q4_0 llama-bench · kb-audit-0903
→ 13-16 tok/s · 8B Q4_K_M Ollama · kb-audit-0903
→ 8-12 tok/s · 13B Q4 band, Pascal kernel-inefficiency · kb-audit-0903
Nominal bandwidth: 484 GB/s — see the effective decode window above
→ qwen3:0.6b on Tesla M60 (16GB, used) ($35) 0.6B · 0.6GB · needs ~1GB (4k ctx)
→ qwen3:1.7b on Tesla M60 (16GB, used) ($35) 1.7B · 1.4GB · needs ~1.8GB (4k ctx)
→ qwen3:4b on Tesla M60 (16GB, used) ($35) 4B · 2.6GB · needs ~3.2GB (4k ctx)
→ llama3.2:1b on Tesla M60 (16GB, used) ($35) 1B · 1.3GB · needs ~1.4GB (4k ctx)
→ llama3.2:3b on Tesla M60 (16GB, used) ($35) 3B · 2.0GB · needs ~2.4GB (4k ctx)
→ gemma3:1b on Tesla M60 (16GB, used) ($35) 1B · 1.0GB · needs ~1.1GB (4k ctx)
→ phi4:mini on Tesla M60 (16GB, used) ($35) mini 3.8B · 2.5GB · needs ~3GB (4k ctx)
→ moondream:2b on Tesla M60 (16GB, used) ($35) 2B · 1.7GB · needs ~3.7GB (4k ctx)
→ whisper:tiny on Tesla M60 (16GB, used) ($35) tiny · 0.1GB · needs ~2.1GB (4k ctx)
→ whisper on Tesla M60 (16GB, used) ($35) base · 0.3GB · needs ~2.3GB (4k ctx)
→ whisper:small on Tesla M60 (16GB, used) ($35) small · 0.9GB · needs ~2.9GB (4k ctx)
→ whisper:medium on Tesla M60 (16GB, used) ($35) medium · 3.1GB · needs ~5.1GB (4k ctx)
→ kokoro:82m on Tesla M60 (16GB, used) ($35) 82M · 0.3GB · needs ~2.3GB (4k ctx)
→ gemma3:4b on Tesla M60 (16GB, used) ($35) 4B · 3.3GB · needs ~3.8GB (4k ctx)
→ deepseek-r1:7b on Tesla M60 (16GB, used) ($35) distill 7B · 4.7GB · needs ~4.9GB (4k ctx)
→ mistral:7b on Tesla M60 (16GB, used) ($35) 7B · 4.1GB · needs ~4.6GB (4k ctx)
→ llava:7b on Tesla M60 (16GB, used) ($35) 7B · 4.7GB · needs ~6.7GB (4k ctx)
→ qwen3:8b on Tesla M60 (16GB, used) ($35) 8B · 5.2GB · needs ~5.8GB (4k ctx)
→ qwen2.5vl:7b on Tesla M60 (16GB, used) ($35) 7B · 5.6GB · needs ~5.8GB (4k ctx)
→ llama3.1:8b on Tesla M60 (16GB, used) ($35) 8B · 4.9GB · needs ~5.4GB (4k ctx)
→ deepseek-r1:8b on Tesla M60 (16GB, used) ($35) distill 8B · 4.9GB · needs ~5.4GB (4k ctx)
→ whisper:large-v3 on Tesla M60 (16GB, used) ($35) large-v3 · 6.2GB · needs ~8.2GB (4k ctx)
→ mistral-nemo:12b on Tesla M60 (16GB, used) ($35) 12B · 7.1GB · needs ~7.7GB (4k ctx)
→ sdxl on Tesla M60 (16GB, used) ($35) SDXL base · 6.9GB · needs ~8.9GB (4k ctx)
→ gemma3:12b on Tesla M60 (16GB, used) ($35) 12B · 8.1GB · needs ~9.6GB (4k ctx)
→ qwen3:14b on Tesla M60 (16GB, used) ($35) 14B · 9.3GB · needs ~9.9GB (4k ctx)
→ deepseek-r1:14b on Tesla M60 (16GB, used) ($35) distill 14B · 9.0GB · needs ~9.8GB (4k ctx)
→ phi4:14b on Tesla M60 (16GB, used) ($35) 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
⚠ Pascal FP16 penalty applies in full — K-quants only
⚠ 250W card — check the used PSU
⚠ 580 driver branch is the last for Pascal
⚠ BENCH ANOMALY: one 8B Ollama row reads 14.5 tok/s (eff ~0.15) — below the Pascal Q4_K_M cluster (0.30-0.47 median); priced as misconfig-class in the 0917 calibration; kept verbatim, band governs.
$1425 — 24GB used value; 32B Q4 fits fully, no offload
$900 — 16GB current-gen with tensor cores and FP8