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OP·Eruo Fredoline
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Some links on this site are affiliate links (Amazon Associates and other first-class retailers). When you buy through them, we earn a small commission at no extra cost to you. Affiliate links do not influence our verdicts — there are cards we rate highly that we don't have affiliate relationships with, and cards that sell well that we refuse to recommend. Read more →

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RUNLOCALAI · v38
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  4. /NVIDIA GeForce RTX 2070
UNIT · NVIDIA · GPU
8 GB VRAMhigh·Reviewed June 2026

NVIDIA GeForce RTX 2070

NVIDIA GeForce RTX 2070 — stylized gpu render
generated
Credit: Generated by Imagen 4 Fast — stylized brand-aware render·License: operator-owned

Turing high-tier. 8 GB VRAM, similar bandwidth to the 2060 Super, slightly more compute. Runs 7B Q4 at ~65-80 tok/s, 13B Q4 with light offload. A solid used-market choice for operators with $200-260 to spend.

Released 2018·~$240 street·448 GB/s memory bandwidth
▼ CHECK CURRENT PRICE· 1 retailer
NVIDIA GeForce RTX 2070
Check on Amazon→

Affiliate disclosure: as an Amazon Associate and partner of other retailers, we earn from qualifying purchases. The verdict on this page is our editorial opinion; affiliate links never influence what we recommend.

RUNLOCALAI SCORE
See full leaderboard →
323/ 1000
CC-tier
Estimated
Throughput
156/ 500
VRAM-fit
80/ 200
Ecosystem
200/ 200
Efficiency
25/ 100

Sub-scores sum to 461 / 1000. Headline = 461 × 0.70 (Estimated-confidence discount) = 323. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →

Extrapolated from 448 GB/s bandwidth — 53.8 tok/s estimated. No measured benchmarks yet.

WORKLOAD FIT
Try other hardware →

Plain-English: Comfortable for 7B chat.

7B chat✓
Comfortable
14B chat✗
Doesn't fit
32B chat✗
Doesn't fit
70B chat✗
Doesn't fit
Coding agent✗
Doesn't fit
Vision (≤8B VLM)~
Tight
Long context (32K)✗
Doesn't fit
✓Comfortable — fits with headroom
~Tight — works, no slack
△Marginal — needs aggressive quant
✗Doesn't fit usefully

Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.

BLK · VERDICT

Our verdict

OP · Eruo Fredoline|VERIFIED JUN 9, 2026
5.1/10

This card is for the operator who needs a reliable, affordable entry into local inference with CUDA-only stacks, and is willing to work within an 8 GB VRAM ceiling. It handles 7B Q4 models at 50-65 tok/s and 13B Q4 with partial offload at 20-30 tok/s, making it a solid choice for chat and code completion on smaller models. The 8 GB VRAM breaks on 30B+ models and struggles with 13B Q4 fully in VRAM, requiring offloading that slows generation. Pass if you need to run 13B models entirely on GPU or plan to scale to 30B+; the 3060 12 GB offers more VRAM for similar money. At ~$240 used, this is a fair value for a CUDA-only rig that prioritizes speed on small models over capacity.

›Why this rating

The RTX 2070 offers good speed for 7B models and a low used price, but its 8 GB VRAM limits model size and forces offloading for 13B. It's a competent entry-level card, not a standout for larger workloads.

BLK · OVERVIEW

Overview

Turing high-tier. 8 GB VRAM, similar bandwidth to the 2060 Super, slightly more compute. Runs 7B Q4 at ~65-80 tok/s, 13B Q4 with light offload. A solid used-market choice for operators with $200-260 to spend.

Retailers we'd check:Amazon

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

BLK · SPECS

Specs

VRAM8 GB
Power draw (peak)175 W
Released2018
MSRP$499
Backends
CUDA
Vulkan

Models that fit

Open-weight models small enough to run on NVIDIA GeForce RTX 2070 with usable context.

all-MiniLM-L6-v2
0.022B · other
Qwen 3 0.6B
0.6B · qwen
BGE Large EN v1.5
0.335B · other
Nomic Embed Text v1.5
0.137B · other
Kokoro 82M
0.082B · other
XTTS v2
0.46B · other
BGE Reranker v2 M3
0.57B · other
all-mpnet-base-v2
0.109B · other

Frequently asked

What models can NVIDIA GeForce RTX 2070 run?

With 8GB VRAM, the NVIDIA GeForce RTX 2070 runs 7B models comfortably in Q4 quantization. See the model list below for tested combinations.

Does NVIDIA GeForce RTX 2070 support CUDA?

Yes — NVIDIA GeForce RTX 2070 is an NVIDIA card with full CUDA support, the most mature local-AI backend. llama.cpp, Ollama, vLLM, and ExLlamaV2 all run natively.

How much does NVIDIA GeForce RTX 2070 cost?

Current street price for NVIDIA GeForce RTX 2070 is around $240 (MSRP $499). Prices vary by region and supply.

Where next?

Buyer guides
  • Best GPU for local AI →
  • Best laptop for local AI →
  • Best Mac for local AI →
  • Best used GPU for local AI →
Troubleshooting
  • CUDA out of memory →
  • Ollama running slowly →
  • ROCm not detected →
  • Model keeps crashing →

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.

Compare alternatives

Hardware worth comparing

The closest alternatives by price, memory bandwidth, and form factor, plus a step up and down — so you can frame the buying decision against real options.

Closest matches
Similar price, bandwidth & form factor
  • AMD Radeon RX 5700 XT
    amd · 8 GB VRAM
    3.5/10
  • NVIDIA GeForce RTX 2070 Super
    nvidia · 8 GB VRAM
    4.8/10
  • NVIDIA GeForce RTX 3060 Ti
    nvidia · 8 GB VRAM
    5.0/10
  • NVIDIA GeForce GTX 1080 Ti
    nvidia · 11 GB VRAM
    6.6/10
  • NVIDIA GeForce RTX 2060 Super
    nvidia · 8 GB VRAM
    4.8/10
  • Intel Arc B580
    intel · 12 GB VRAM
    6.3/10
Step up
More capable — more memory or a higher tier
  • NVIDIA GeForce GTX 1080 Ti
    nvidia · 11 GB VRAM
    6.6/10
  • AMD Radeon RX 6700 XT
    amd · 12 GB VRAM
    6.8/10
  • Intel Arc B580
    intel · 12 GB VRAM
    6.3/10
Step down
Lighter — cheaper or more constrained
  • NVIDIA GeForce RTX 2060 Super
    nvidia · 8 GB VRAM
    4.8/10
  • Intel Arc B580
    intel · 12 GB VRAM
    6.3/10
  • AMD Radeon RX 6650 XT
    amd · 8 GB VRAM
    5.1/10