Consumer GPUNVIDIA

NVIDIA GeForce RTX 4070 Ti 12GB

Curated Aggregate·2024-08-056 workloads · 6 records
MyAI Rating8.4tok/s · Phi-3 Mini

VRAM

12 GB

TDP

285 W

MSRP

$799

Perf/W

0.27 tok/s/W

Cost/1K tok

$0.11/M

Tested

2024-08-05

Quick answer

How fast is NVIDIA GeForce RTX 4070 Ti 12GB for local AI workloads?

NVIDIA GeForce RTX 4070 Ti 12GB hits 165.0 tok/s on Phi-3 Mini, its strongest benchmarked workload (batch 1, 4096-token context, 12GB VRAM, 285W TDP). It has 6 records across 6 workloads in our database, with a MyAI Rating of 8.4/10. Llama 3 70B Q4 needs ~40GB VRAM, so check the VRAM column before assuming feasibility.

Source: MyAIHardware benchmark database (bench-rtx4070ti-phi3)As of 2024-04-23

Overview

Mid-Range 12GB

NVIDIA GeForce RTX 4070 Ti 12GB — Ada Lovelace upper-midrange. 12GB GDDR6X at 504 GB/s, 285W TDP. Faster compute than the 3080 but same 12GB VRAM ceiling.

AI Usefulness

12GB caps at 7B Q4 comfortably, 13B Q4 with tight context. Good for 7B coding agents and image generation. The 12GB ceiling is the limitation — if you're serious about local AI, the extra $100-200 for a used 3090 (24GB) is transformative. Best as a gaming-primary, AI-secondary card.

Editorial Verdict

6.5
Editor Rating

Buy as a gaming-primary, AI-secondary card. For pure AI, the 12GB ceiling is limiting. Consider used 3090 (24GB) or 4060 Ti 16GB instead.

What it does well

  • +12GB GDDR6X at 504 GB/s — fast Ada compute in midrange tier
  • +285W TDP — balances performance and power well
  • +Full Ada feature set with FP8 acceleration
  • +Good gaming+AI crossover card

Where it breaks

  • , 12GB VRAM — the ceiling for serious local AI
  • , 13B Q4 fits but with tight context
  • , Used 3090 at similar price gives double the VRAM
  • , $799 MSRP competes with used 3090 territory

Sweet Spot

7-8B Q4 full-GPU at 65+ tok/s — excellent for coding agents on small models and image generation

Bad Use Cases

  • ×32B+ models (need 16GB+ card)
  • ×70B models (need 24GB+ card)
  • ×Pure AI builds (used 3090 is better $/VRAM)
  • ×Budget AI builds (4060 Ti 16GB is better $/VRAM)

What Breaks First

12GB VRAM ceiling — this is the hard limit. 13B Q4 fits but leaves minimal context headroom. 32B Q4 won't fit usefully.

Software Support

Ollamallama.cppvLLMExLlamaV2LM StudioPyTorch

Ubuntu 24.04 (excellent), Windows 11 (excellent), WSL2 (excellent), macOS (unsupported)

Best Pairings

  • Ollama + Llama 3.1 8B Q4 for general use and coding
  • ComfyUI + SDXL for image generation alongside LLM
  • 650W Gold PSU

Power & Cooling

285W TGP. 650W PSU minimum. Reasonable thermals. Good efficiency for Ada-class compute.

Verdict

NVIDIA GeForce RTX 4070 Ti 12GB with 12GB VRAM at 285W TDP, scored across 6 workloads with 6 benchmark records.

Best workload

Phi-3 Mini

165 tok/s

Quantization

Q4_K_M

4K context · batch 1

LLM Inference Performance

04590135180Llama 38B Q4Phi-3 MiniMistral 7BGemma 29BAsure 12B

Benchmarks (6 workloads)

WorkloadScoreQuantContextσStatusTested
Llama 3 8B Q4

llm

78.0tok/sQ4_K_M4K, Curated Aggregate2024-08-05
Phi-3 Mini

llm

165.0tok/sQ4_K_M4K, Curated Aggregate2024-04-23
SDXL image gen

image

11.0img/minFP16, , Curated Aggregate2025-08-01
Mistral 7B

llm

89.0tok/sQ4_K_M4K, Curated Aggregate2024-06-08
Gemma 2 9B

llm

75.0tok/sQ4_K_M8K, Curated Aggregate2024-08-18
Asure 12B

llm

44.0tok/sQ4_K_M8K±2.5Curated Aggregate2025-05-01

MyAI Score

Flagship
8.4/10

NVIDIA GeForce RTX 4070 Ti 12GB clears a 8.4/10 based on workload-normalized throughput, memory headroom, efficiency, value, trust, and coverage.

Throughput
278
Capability
122
Efficiency
46
Value
56
Trust
44
Coverage
40
Composite benchmark586 / 1000

Workload Fit

What models fit this 12GB card at different quantization levels.

Q4
Q8
FP16
7-8B
Excellent
Excellent
Won't fit
13-14B
Excellent
Tight
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 Mini165 tok/s
Mistral 7B89 tok/s
Llama 3 8B Q478 tok/s

Source

Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — small model fits comfortably.

View sourceHow we benchmark →

Public Trust Layer

Trust score

6/10

MyAI rating

8.4

Runs

1

Freshness

Stale

Source-linked row with explicit verification status.

Tested on 2024-04-23; 856 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 $799 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.