ASICGoogle

Google TPU v5p

Name: Google TPU v5p
Brand: Google
SKU: tpu-v5p-trillium

Curated Aggregate·2024-08-194 workloads · 4 records

MyAI RatingNot scoredInsufficient comparison evidence

VRAM

95 GB

TDP

700 W

MSRP

$38k

Perf/W

0.16 tok/s/W

Cost/1K tok

$0.0037/k

Tested

2024-08-19

Quick answer

How fast is Google TPU v5p for local AI workloads?

Google TPU v5p has a source-attributed result of 410.0 tok/s on Llama 3 8B FP16 (batch 1, 8192-token context, FP16; 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.

Verdict

Google TPU v5p with 95GB VRAM at 700W TDP, scored across 4 workloads with 4 benchmark records.

Reference workload

Llama 3 8B FP16

410 tok/s

Quantization

FP16

8K context · batch 1

LLM Inference Performance

Benchmarks (4 workloads)

Workload	Score	Quant	Context	σ	Status	Tested
Llama 3 70B Q8 llm	110.0tok/s	INT8	8K	,	Curated Aggregate	2024-08-19
Llama 3 8B FP16 llm	410.0tok/s	FP16	8K	,	Curated Aggregate	2026-01-08
Mistral 7B llm	410.0tok/s	INT8	4K	,	Curated Aggregate	2024-12-11
Qwen 2.5 14B llm	195.0tok/s	INT8	8K	,	Curated Aggregate	2025-02-20

MyAI Score: not scored

There is insufficient comparable evidence to score Google TPU v5p. A score needs results for multiple devices with matching workload, runtime version, quantization, context and batch size.

Workload Fit

Planning estimates for 95GB: weights plus at least 2GB or 10% overhead. Actual KV cache depends on model, context and cache format; confirm the artifact before buying.

FP16

7-8B

Excellent

13-14B

Excellent

32B

Excellent

70B

Excellent

Good

Won't fit

Reported batch-one examples

Llama 3 8B FP16410 tok/s

Qwen 2.5 14B195 tok/s

Mistral 7B410 tok/s

Source

Vendor benchmark.

View source How 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: 2026-01-08; 244 days old.

Open primary source

Enterprise buying note

Where to buy

Reseller compare

Retailer we'd check first

Amazon search plus reseller quotes

For datacenter and accelerator parts, Amazon is useful for spotting live listings, accessories, or used pulls, but serious procurement usually happens through integrators, brokers, or cloud partners.

Enterprise procurement, not retail

This silicon is typically acquired through an authorized OEM partner, system integrator, or hyperscaler reseller. For on-demand access, compare hourly rates at RunPod, Vast.ai, Lambda Labs, or your existing cloud provider before committing to capital expenditure.

+Use $38k only as a rough anchor. Enterprise street pricing moves with supply, warranty, and included accessories.
+Confirm cooling, power delivery, and return terms before you purchase. These parts often ship without consumer-friendly safeguards.
+If this is for production, compare against authorized reseller quotes before you commit.

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

Affiliate note: we do not have a device-level ASIN yet, so this opens tagged Amazon search results for the exact product name.

Same Tier

Groq LPU (per chip)

0.23GB

Cerebras WSE-3 (CS-3)