Consumer GPUNVIDIA

NVIDIA GeForce RTX 5060 8GB

Curated Aggregate·2026-04-253 workloads · 4 records
MyAI Rating8.1tok/s · Phi-3 Mini

VRAM

8 GB

TDP

145 W

MSRP

$299

Perf/W

0.33 tok/s/W

Cost/1K tok

$0.07/M

Tested

2026-04-25

Quick answer

How fast is NVIDIA GeForce RTX 5060 8GB for local AI workloads?

NVIDIA GeForce RTX 5060 8GB hits 145.0 tok/s on Phi-3 Mini, its strongest benchmarked workload (batch 1, 4096-token context, 8GB VRAM, 145W TDP). It has 4 records across 3 workloads in our database, with a MyAI Rating of 8.1/10. Llama 3 70B Q4 needs ~40GB VRAM, so check the VRAM column before assuming feasibility.

Source: MyAIHardware benchmark database (bench-x3-rtx5060-phi3)As of 2025-04-25

Verdict

NVIDIA GeForce RTX 5060 8GB with 8GB VRAM at 145W TDP, scored across 3 workloads with 4 benchmark records.

Best workload

Phi-3 Mini

145 tok/s

Quantization

Q4_K_M

4K context · batch 1

LLM Inference Performance

04080120160Llama 38B Q4Phi-3 MiniMistral 7B

Benchmarks (3 workloads)

WorkloadScoreQuantContextσStatusTested
Llama 3 8B Q4

llm

48.0tok/sQ4_K_M2K, Curated Aggregate2026-04-25
Phi-3 Mini

llm

145.0tok/sQ4_K_M4K, Curated Aggregate2025-04-25
Mistral 7B

llm

64.0tok/sQ4_K_M4K, Curated Aggregate2025-05-02

MyAI Score

Enthusiast
8.1/10

NVIDIA GeForce RTX 5060 8GB clears a 8.1/10 based on workload-normalized throughput, memory headroom, efficiency, value, trust, and coverage.

Throughput
279
Capability
109
Efficiency
59
Value
38
Trust
49
Coverage
20
Composite benchmark554 / 1000

Workload Fit

What models fit this 8GB card at different quantization levels.

Q4
Q8
FP16
7-8B
Excellent
Tight
Won't fit
13-14B
Tight
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
Top Benchmarks
Phi-3 Mini145 tok/s
Mistral 7B64 tok/s
Llama 3 8B Q448 tok/s

Source

Blackwell entry.

View sourceHow we benchmark →

Public Trust Layer

Trust score

6/10

MyAI rating

8.1

Runs

1

Freshness

Stale

Source-linked row with explicit verification status.

Tested on 2025-04-25; 489 days old.

Open primary source

Best place to start

Where to buy

Search first

Retailer we'd check first

Amazon search

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

Search Amazon listings
  • +Use $299 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.

This is a strong buy when the listing stays close to reference pricing and matches the workload you actually run.

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