01
Gemma 4 E4B
Gemma / 4.5B / Q4_K_M / ~4 GB
Best for: On-device, Mobile, Chat·Pop: 82/100
Perf: ~49 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.
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.
Quick answer
The best local LLM for the RTX 4060 is Gemma 4 E4B at ~49 tok/s on its 8GB VRAM. It uses ~4GB of VRAM; the RTX 4060 handles up to 8B parameter models at Q4. A 14B model runs at ~4 tok/s with CPU offload.
$ollama run gemma4:e4b
TOP PICK
Gemma 4 E4B
EST. SPEED
~49 tok/s
VRAM NEEDED
~4 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 4060 Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 8GB |
|---|---|---|
| 7B | ~34 tok/s | Fits in VRAM |
| 14B | ~4 tok/s | CPU offload (slow) |
| 20B MoE (3.6B active) | ~10 tok/s | CPU offload (slow) |
| 32B | ~1 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~9 tok/s | CPU offload (slow) |
| 70B | ~0 tok/s | CPU offload (slow) |
| 120B MoE (5.1B active) | ~4 tok/s | CPU offload (slow) |
ModelFit estimates, not measured benchmarks: anchored to an 8B-class Q4_K_M model at 16K context on the RTX 4060's 272 GB/s bandwidth, then scaled by model size. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 8GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident.
Context costs VRAM too. Gemma 4 E4B loads ~4 GB of weights; at 16k context the KV cache adds ~2.0 GB (still fits the ~7 GB usable VRAM), and at 64k it adds ~8.0 GB (exceeds the budget, use a smaller quant or a q8_0 KV cache).
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
Where to Buy the RTX 4060
≈ $299 streetStorage & accessories for your model library
Internal NVMe SSD · 2TB~$170
A Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.
Check price on AmazonUSB4 NVMe Enclosure~$80
40Gbps external storage fast enough to run models from. Pair it with an M.2 drive for a portable model vault.
Check price on AmazonModelFit may earn a commission on purchases through these links, at no extra cost to you. Prices shown are approximate street references.
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.
What Does Not Fit in 8GB (And What It Costs You)
With 8GB, the honest ceiling is a single 7B model at Q4. Anything larger does not simply run slower: the layers that do not fit spill to system RAM over PCIe, and decode speed falls off a cliff. These are the same bandwidth-derived estimates used in the table above, shown here as the penalty rather than the headline.
| Model Size | Fits in 8GB? | Est. Speed | Slowdown vs 7B |
|---|---|---|---|
| 7B | Yes | ~34 tok/s | n/a |
| 14B | No, needs ~2GB more in system RAM | ~4 tok/s | ~9x slower |
| 20B MoE (3.6B active) | No, needs ~5GB more in system RAM | ~10 tok/s | ~3x slower |
| 32B | No, needs ~12GB more in system RAM | ~1 tok/s | ~34x slower |
| 35B MoE (3B active) | No, needs ~14GB more in system RAM | ~9 tok/s | ~4x slower |
Mixture-of-experts models are the exception worth knowing: they activate only a fraction of their parameters per token, so a large MoE can stay usable on a small card where a dense model of the same total size will not. Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 4060 vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 3060 | 12 GB | 42 tok/s | 360 GB/s | $250 |
| RTX 4060 | 8 GB | 30 tok/s | 272 GB/s | $299 |
| RTX 4060 Ti | 16 GB | 34 tok/s | 288 GB/s | $409 |
| RTX 5060 Ti | 16 GB | 51 tok/s | 448 GB/s | $430 |
Recommended Models
registry-verified10 models02
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: ~60 tok/s · first token ~0.4s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for on-device agents, tool calling, multilingual chat on RTX 4060.
03
Qwen2.5 Coder 7B
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Coding·Pop: 72/100
Perf: ~34 tok/s · first token ~0.4s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for coding on RTX 4060.
04
DeepSeek-R1 Distill Qwen 7B
DeepSeek / 7B / Q4_K_M / ~5.5 GB
Best for: Reasoning, Coding·Pop: 68/100
Perf: ~34 tok/s · first token ~0.4s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for reasoning, coding on RTX 4060.
05
Qwen2.5 7B Instruct
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 72/100
Perf: ~34 tok/s · first token ~0.4s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for chat, coding on RTX 4060.
06
Mistral 7B Instruct
Mistral / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 74/100
Perf: ~34 tok/s · first token ~0.4s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for chat, coding on RTX 4060.
07
Granite 4.1 8B Instruct
Granite / 8B / Q4_K_M / ~5.5 GB
Best for: Enterprise assistant, tool calling, instruction following·Pop: 62/100
Perf: ~30 tok/s · first token ~0.5s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for enterprise assistant, tool calling, instruction following on RTX 4060.
08
Qwen3.5 4B Instruct
Qwen / 4B / Q4_K_M / ~3.5 GB
Best for: Coding, Agents, Multimodal·Pop: 88/100
Perf: ~54 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.
09
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~27 tok/s · first token ~0.5s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4060.
10
Qwen3 8B
Qwen / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 88/100
Perf: ~30 tok/s · first token ~0.5s
Local OKOK
Fits in 8 GB VRAM with room to spare. Best for chat, coding on RTX 4060.
Models Too Big for 8GB? Rent a Cloud GPU
by the hourThe 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, billed by the hour, no hardware purchase needed.
RunPodHourly GPU pods (RTX 4090 to H100) with one-click Ollama/vLLM templates.Rent
Vast.aiMarketplace of rented GPUs, usually the cheapest per-hour prices.Rent
ModelFit may earn a commission on sign-ups made through these links, at no extra cost to you.
Similar GPUs for Local AI
Compatible Model Families
Qwen
Alibaba Cloud: Widest size range (0.5B to 235B)
LlamaMeta: Most popular open-weight model family
DeepSeekDeepSeek AI: Best-in-class reasoning with R1 models
MistralMistral AI: Excellent performance-per-parameter ratio
GemmaGoogle DeepMind: Excellent quality at small sizes (1B-9B)
PhiMicrosoft: 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.
How fast is a 27B-class model on the RTX 4060?
The RTX 4060's 8GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 7B, at an estimated 34 tok/s.
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