NPUIntel

Intel Core Ultra 9 285K NPU

Curated Aggregate·2024-05-232 workloads · 4 records
MyAI RatingNot scoredInsufficient comparison evidence

VRAM

0 GB

TDP

15 W

MSRP

$589

Perf/W

0.93 tok/s/W

Cost/1K tok

$0.44/M

Tested

2024-05-23

Quick answer

How fast is Intel Core Ultra 9 285K NPU for local AI workloads?

Intel Core Ultra 9 285K NPU has a source-attributed result of 24.0 tok/s on Phi-3 Mini (batch 1, 4096-token context, Q4_K_M; 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-x3-cu9-285k-phi3)As of 2024-11-09

Verdict

Intel Core Ultra 9 285K NPU with 0GB VRAM at 15W TDP, scored across 2 workloads with 4 benchmark records.

Reference workload

Phi-3 Mini

24 tok/s

Quantization

Q4_K_M

4K context · batch 1

LLM Inference Performance

06121824Phi-3 MiniMistral 7B

Benchmarks (2 workloads)

WorkloadScoreQuantContextσStatusTested
Phi-3 Mini

llm

24.0tok/sQ4_K_M4K, Curated Aggregate2024-11-09
Mistral 7B

llm

9.0tok/sQ4_K_M4K, Curated Aggregate2024-10-25

MyAI Score: not scored

There is insufficient comparable evidence to score Intel Core Ultra 9 285K 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 Mini24 tok/s
Mistral 7B9 tok/s

Source

Phi-3 CPU.

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: 2024-11-09; 669 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 $589 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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