will it local
X1-45 is the CUT-DOWN Adreno: 1.7 TFLOPs vs 4.6 on X1-85 in X1P-42-100 (wccftech SKU table) — ~280 MHz-class clock; the X1P-26-100 variant ships an even slower-clocked X1-45 Same llama.cpp story as X1-85: OpenCL backend technically lists Adreno support, but no X1-45-specific verified bench was found — expect CPU to beat iGPU offload at this power class (same conclusion as the X1-85 arXiv study) Memory bandwidth is unchanged from X Elite (LPDDR5X-8448 = 135.2 GB/s per TechPowerUp X1P-42-100/X1P-26-100 pages) — bandwidth is not the bottleneck; shader count/clock is Hexagon NPU still rated 45 TOPS but inaccessible from llama.cpp (no QNN backend) — don't sell this as an 'AI 45 TOPS' machine for local LLMs Soldered LPDDR5X (8/16/32 GB typical on X Plus laptops), never upgradeable
Shared-memory device — speed scales with RAM bandwidth (135.2 GB/s shared RAM), usable for inference ≈ 13.5 GB of 16 GB system RAM
→explore the full catalogupgrade paths with live prices · what fits on each machinecomputed band 6.6-21.0 tok/s for 8B Q4_K_M (roofline, 135.2 GB/s shared RAM)
Effective decode window: 0.25–0.80 of 135.2 GB/s nominal → ~34–108 GB/s effective (llama.cpp decode, Q4_K_M basis; arch window, community-calibrated; nominal is config-arithmetic shared-RAM bandwidth)
→ 13b-q4 offload-partial 14.6 GB vs 13.5 GB usable — partial CPU offload, expect large speed loss
→ 3b-q4 full 2.8 GB needed of 13.5 GB usable — headroom for context
→ 4b-q4 full 3.7 GB needed of 13.5 GB usable — headroom for context
→ 8b-q4 full 6.1 GB needed of 13.5 GB usable — headroom for context
no single-card bench published — band is computed (see notes)
Nominal bandwidth: 135.2 GB/s shared memory — 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 20B MoE — 14.2 of 13.5 GB usable — barely over; real with q8 KV cache (halves KV) or shorter context
→ devstral:24b 24B — 15.2 GB vs 13.5 GB usable — partial CPU offload, expect large speed loss
⚠ X1-45 is the CUT-DOWN Adreno: 1.7 TFLOPs vs 4.6 on X1-85 in X1P-42-100 (wccftech SKU table) — ~280 MHz-class clock; the X1P-26-100 variant ships an even slower-clocked X1-45
⚠ Same llama.cpp story as X1-85: OpenCL backend technically lists Adreno support, but no X1-45-specific verified bench was found — expect CPU to beat iGPU offload at this power class (same conclusion as the X1-85 arXiv study)
⚠ Memory bandwidth is unchanged from X Elite (LPDDR5X-8448 = 135.2 GB/s per TechPowerUp X1P-42-100/X1P-26-100 pages) — bandwidth is not the bottleneck; shader count/clock is
⚠ Hexagon NPU still rated 45 TOPS but inaccessible from llama.cpp (no QNN backend) — don't sell this as an 'AI 45 TOPS' machine for local LLMs
⚠ Soldered LPDDR5X (8/16/32 GB typical on X Plus laptops), never upgradeable
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