NPUIntel

Intel Core Ultra 9 288V (Lunar Lake) NPU

Curated Aggregate·2026-02-262 workloads · 3 records
MyAI RatingNot scoredInsufficient comparison evidence

VRAM

0 GB

TDP

17 W

MSRP

$549

Perf/W

1.29 tok/s/W

Cost/1K tok

$0.26/M

Tested

2026-02-26

Quick answer

How fast is Intel Core Ultra 9 288V (Lunar Lake) NPU for local AI workloads?

Intel Core Ultra 9 288V (Lunar Lake) NPU has a source-attributed result of 22.0 tok/s on Phi-3 Mini (batch 1, 2048-token context, INT4; runtime not documented). This is a reference report, not an independently verified lab result. A 70B Q4_K_M artifact needs roughly 40GB for weights alone; smaller memory configurations require explicit offload or model splitting and do not establish full-GPU residency.

Source: MyAIHardware benchmark database (bench-cu288v-phi3)As of 2026-02-26

Verdict

Intel Core Ultra 9 288V (Lunar Lake) NPU with 0GB VRAM at 17W TDP, scored across 2 workloads with 3 benchmark records.

Reference workload

Phi-3 Mini

22 tok/s

Quantization

INT4

2K context · batch 1

LLM Inference Performance

06121824Phi-3 MiniLlama 3 8BQ4

Benchmarks (2 workloads)

WorkloadScoreQuantContextσStatusTested
Phi-3 Mini

llm

22.0tok/sINT42K, Curated Aggregate2026-02-26
Llama 3 8B Q4

llm

9.0tok/sINT42K, Curated Aggregate2026-02-18

MyAI Score: not scored

There is insufficient comparable evidence to score Intel Core Ultra 9 288V (Lunar Lake) NPU. A score needs results for multiple devices with matching workload, runtime version, quantization, context and batch size.

Workload Fit

Planning estimates for 0GB: weights plus at least 2GB or 10% overhead. Actual KV cache depends on model, context and cache format; confirm the artifact before buying.

Q4
Q8
FP16
7-8B
Won't fit
Won't fit
Won't fit
13-14B
Won't fit
Won't fit
Won't fit
32B
Won't fit
Won't fit
Won't fit
70B
Won't fit
Won't fit
Won't fit
Reported batch-one examples
Phi-3 Mini22 tok/s
Llama 3 8B Q49 tok/s

Source

OpenVINO — 48 TOPS NPU 4.

View sourceHow we benchmark →

Public Trust Layer

Trust score

6/10

MyAI rating

Not scored

Runs

Not documented

Freshness

Stale

Source-linked row with explicit verification status.

Record date: 2026-02-26; 195 days old.

Open primary source

Best place to start

Where to buy

Most buyers

Retailer we'd check first

Amazon

For mainstream AI hardware, Amazon usually updates street pricing, seller availability, and shipping speed faster than most comparison sites.

View current Amazon listing
  • +Use $549 as your price anchor unless the part is clearly supply-constrained or newly launched.
  • +Check the exact cooler, board partner, or memory configuration before buying. The silicon may match, but noise and thermals do not.
  • +If the Amazon price looks inflated, wait or compare against recent street pricing rather than paying a panic premium.

There is insufficient comparable evidence for a purchase recommendation. Check model fit, software support and a current seller quote.

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