Best Local AI Models for RTX 4060 (8GB)

The RTX 4060 is the most affordable current-gen NVIDIA GPU, but its 8GB VRAM is the real constraint for local AI. It runs 7B-8B models at Q4 with little room for context, landing around 30 tokens per second. For anything bigger, the 16GB RTX 4060 Ti or a rented cloud GPU is the smarter path.

8GB VRAM
Quick answer

For the RTX 4060 (8GB VRAM), the best local LLM is Qwen3.5 4B Instruct at ~54.1 tok/s (est.). It uses ~3.5GB of VRAM; the RTX 4060 handles up to 8b parameter models at Q4.

$ollama run qwen3.5:4b
TOP PICK
Qwen3.5 4B Instruct
EST. SPEED
~54.1 tok/s
VRAM NEEDED
~3.5 GB

Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.

VRAM8 GB GDDR6
Speed (8B Q4)30 tok/s
Bandwidth272 GB/s
ArchitectureAda Lovelace
Price$299
Max model sizeUp to 8B parameter models
Compatibility10 excellent, 0 workable

RTX 4060 Estimated Tokens/sec by Model Size

Q4_K_M · ModelFit estimate
Model SizeEst. SpeedFit on 8GB
7B~34 tok/sFits in VRAM
14B~4 tok/sCPU offload (slow)
32B~2 tok/sCPU offload (slow)
70B~1 tok/sCPU offload (slow)

ModelFit estimates from the RTX 4060's 272 GB/s bandwidth and model size at Q4_K_M — not measured benchmarks. "CPU offload" sizes exceed the 8GB VRAM and run far slower than the figure shown.

Where to Buy the RTX 4060

$299
Check price on Amazon ↗

ModelFit may earn a commission on purchases made through these links, at no extra cost to you. Recommendations are based on local-AI performance, not commissions.

RTX 4060 VRAM for AI: What Actually Fits?

8GB GDDR6 at 272 GB/s holds one 7B-8B model at Q4 quantization — Qwen 2.5 7B or Llama 3.2 8B use ~5-5.6GB, leaving only 2-3GB for the KV cache and context. That caps practical context length and rules out 14B models without heavy CPU offloading (which cuts speed 70-80%). The RTX 4060 is a fine entry point for small models, but 8GB is the spec that frustrates you first. If you expect to run 14B models, start with the RTX 4060 Ti 16GB instead.

RTX 4060 vs Similar GPUs

HardwareMemorySpeedBandwidthPrice
RTX 306012 GB42 tok/s360 GB/s$250
RTX 40608 GB30 tok/s272 GB/s$299
RTX 4060 Ti16 GB34 tok/s288 GB/s$409
RTX 5060 Ti16 GB51 tok/s448 GB/s$430

Recommended Models

registry-verified10 models
01

Qwen3.5 4B Instruct

Qwen / 4B / Q4_K_M / ~3.5 GB

Best for: Coding, Agents, Multimodal·Pop: 88/100

Perf: ~54.1 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for coding, agents, multimodal on RTX 4060.

ollamaregistry-verified
02

Gemma 4 E4B

Gemma / 4.5B / Q4_K_M / ~4 GB

Best for: On-device, Mobile, Chat·Pop: 82/100

Perf: ~48.9 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for on-device, mobile, chat on RTX 4060.

ollamaregistry-verified
03

Gemma 3 4B Instruct

Gemma / 4B / Q4_K_M / ~3.5 GB

Best for: Chat, Coding·Pop: 81/100

Perf: ~54.1 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for chat, coding on RTX 4060.

ollamaregistry-verified
04

Gemma 4 E2B

Gemma / 2.3B / Q4_K_M / ~2.3 GB

Best for: IoT, Mobile, Edge·Pop: 76/100

Perf: ~86.6 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for iot, mobile, edge on RTX 4060.

ollamaregistry-verified
05

Phi-4 Mini 3.8B

Phi / 3.8B / Q4_K_M / ~3.2 GB

Best for: Coding, Chat·Pop: 75/100

Perf: ~56.5 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for coding, chat on RTX 4060.

ollamaregistry-verified
06

Llama 3.2 3B Instruct

Llama / 3B / Q4_K_M / ~2.5 GB

Best for: Chat·Pop: 72/100

Perf: ~69.1 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for chat on RTX 4060.

ollamaregistry-verified
07

Phi-3 Mini 3.8B

Phi / 3.8B / Q4_K_M / ~3.2 GB

Best for: Coding, Chat·Pop: 64/100

Perf: ~56.5 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for coding, chat on RTX 4060.

ollamaregistry-verified
08

Qwen2.5 3B Instruct

Qwen / 3B / Q4_K_M / ~2.5 GB

Best for: Chat, Coding·Pop: 64/100

Perf: ~69.1 tok/s · first token ~0.4s

Local OKExcellent

Fits in 8 GB VRAM with room to spare. Best for chat, coding on RTX 4060.

ollamaregistry-verified
09

Qwen3 8B

Qwen / 8B / Q4_K_M / ~6.5 GB

Best for: Chat, Coding·Pop: 88/100

Perf: ~30.0 tok/s · first token ~0.5s

Local OKOK

Fits in 8 GB VRAM with room to spare. Best for chat, coding on RTX 4060.

ollamaregistry-verified
10

LFM2.5 8B-A1B

LFM2 / 8.3B / Q4_K_M / ~5.5 GB

Best for: On-device agents, tool calling, multilingual chat·Pop: 72/100

Perf: ~29.1 tok/s · first token ~0.5s

Local OKOK

Fits in 8 GB VRAM with room to spare. Best for on-device agents, tool calling, multilingual chat on RTX 4060.

ollamaregistry-verified

Models Too Big for 8GB? Rent a Cloud GPU

by the hour

The RTX 4060 tops out around up to 8b parameter models. For anything bigger, an hourly rented GPU runs the same open weights with the same Ollama workflow — no hardware purchase, billed by the hour.

Rent on RunPod ↗Rent on Vast.ai ↗

RunPod: Hourly GPU pods (RTX 4090 to H100) with one-click Ollama/vLLM templates.

Vast.ai: Marketplace of rented GPUs — usually the cheapest per-hour prices.

ModelFit may earn a commission on sign-ups made through these links, at no extra cost to you.

Similar GPUs for Local AI

RTX 4060 Ti (16GB · 34 tok/s)RTX 3060 (12GB · 42 tok/s)RTX 5060 Ti (16GB · 51 tok/s)

Compatible Model Families

Qwen

Alibaba Cloud — Widest size range (0.5B to 235B)

Llama

Meta — Most popular open-weight model family

DeepSeek

DeepSeek AI — Best-in-class reasoning with R1 models

Mistral

Mistral AI — Excellent performance-per-parameter ratio

Gemma

Google DeepMind — Excellent quality at small sizes (1B-9B)

Phi

Microsoft — Best quality-per-gigabyte at small sizes

RTX 4060 FAQ: Common Questions

How much VRAM does the RTX 4060 have for LLMs?

The RTX 4060 has 8GB GDDR6 VRAM. After driver and OS overhead, about 7.5GB is usable for model loading. That fits a single 7B-8B model at Q4 quantization, but leaves limited room for long context windows.

What size LLM can I run on an RTX 4060?

Up to 8B parameters at Q4 quantization, and even then context is tight. Good picks are Qwen 2.5 7B (~5.2GB), Llama 3.2 8B (~5.6GB), and Mistral 7B (~4.4GB). 14B models require CPU offloading, which makes them very slow.

Is the RTX 4060 good for local AI in 2026?

It works for 7B-8B models, but 8GB VRAM is limiting. For $100-150 more, the RTX 4060 Ti 16GB or a used RTX 3060 12GB gives meaningfully more headroom. Buy the 4060 only if you already own it or are on a strict budget.

RTX 4060 vs RTX 4060 Ti for running LLMs?

The RTX 4060 Ti 16GB is the better AI card — double the VRAM lets it run 14B models the base 4060 cannot. The 4060 is fine for 7B models, but if local AI is your goal, the 16GB 4060 Ti is worth the extra cost.

Related Guides & Benchmarks

How to Install Ollama — Complete Setup Guide

Step-by-step Ollama installation for beginners on any platform.

Qwen 3.5 Small Models: 4B Beats 20B

Small models that run great on 8GB GPUs like the RTX 4060.

Local LLMs vs GPT-4 and Claude: How They Compare

See how local 7B-8B models on your GPU compare to cloud APIs.

Sizing Local AI? Start With RAM & VRAM

How Much RAM (or VRAM) Do You Need for a Local LLM?

The model-size-to-memory matrix — what each VRAM and RAM tier actually runs.

Best LLM for MacBook (Apple Silicon)

Unified-memory Macs run bigger models per dollar — picks by RAM tier, M1 to M4.

Browse All NVIDIA GPUs for AI

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