Best local LLMs for the RTX 4060 (8 GB) — updated July 2026
The RTX 4060's 8 GB of VRAM runs dense models up to about 9B fully on the GPU at
Q4_K_M, and — the part most guides skip — an MoE like
Qwen3.6 35B-A3B with its experts offloaded to system RAM, because only
~3B parameters are active per token. Being honest: a dense 27B or 70B is out of reach on
this card at any speed you'd actually use.
Numbers you can trust
TurboLLM measures real tokens/sec on your card from actual generation and
shows a VRAM-fit verdict before you load anything. Every speed on this page
is labeled with its source — none of them are guesses, and none are ours: our own
founder-measured numbers come from a different card (an RTX 5070 Ti 16 GB).
What to run on 8 GB
The shortlist from our models hub's 8 GB tier, with fit lines specific to this card.
Qwen3 8B
by Alibaba · GGUF
Dense
toolsthinkingcode
The daily driver for this card — a fast, capable dense all-rounder with a toggleable thinking mode. If you load one model on a 4060, load this.
RTX 4060:Q4_K_M (5.0 GB) fully in VRAM, ~3 GB left for the KV cache — a healthy context, but the model's full 128K won't fit in 8 GB. TurboLLM's fit verdict shows the real limit before you load.
The stretch pick. Only ~3B params are active per token, so the idle experts can live in system RAM while attention and the KV cache stay on the GPU. It shines on 16 GB+, but it does run here.
RTX 4060:Q3_K_M (16.6 GB) with heavy expert offload — most of that file sits in system RAM, so you need it free · TurboLLM's auto-fit sets the split
We haven't benchmarked this card ourselves, so the table below is what the community
reports — not our measurements. The RTX 4060's ~272 GB/s of memory bandwidth is the
limiting factor for generation speed, which is why 7–8B models feel snappy and anything
that spills past 8 GB falls off a cliff.
Model class
Quant
Reported generation speed
7–8B dense (Qwen2.5 7B, Llama 3.1 8B, Mistral 7B)
Q4
~41–51 t/s
9B dense (Gemma 2 9B)
Q4
~18 t/s
13B dense — spills past 8 GB into system RAM
Q4
~8 t/s
Community-reported, databasemart.com Ollama GPU benchmarks (RTX 4060, llama.cpp backend), 2025–2026. Your numbers will differ — TurboLLM measures the real figure on your machine the first time you load a model.
To be fair to the alternatives: LM Studio and Ollama run these same models on a 4060
perfectly well. What they won't show you is a measured tokens/sec per model — LM Studio's
catalog gives a heuristic fit estimate before download, but neither shows a speed measured
from real generation on your card. That's the part TurboLLM does differently.
How TurboLLM auto-fits the RTX 4060
When you load a model, TurboLLM runs a quick benchmark on your GPU and records the real
tokens/sec next to it — measured from actual generation, never estimated. Before you load,
a VRAM-fit verdict tells you whether the file fits 8 GB fully, fits with offload, or won't
fit at all. For the MoE stretch pick, auto-fit sets the GPU-layer count and the CPU-expert
offload split for you — no flag guessing. Details in auto-tune.
FAQ
Can the RTX 4060 run a 70B model?
Not at a usable speed. A 70B needs roughly 40 GB at Q4, so on an 8 GB card almost all of it sits in system RAM, and dense models are memory-bandwidth-bound. The realistic big-model path on this card is an MoE like Qwen3.6 35B-A3B with its experts offloaded to system RAM — only ~3B parameters are active per token.
What quant should I use on 8 GB of VRAM?
Q4_K_M for 7–9B dense models — it delivers the best quality per gigabyte and fits fully in VRAM with room for context. Only drop below Q4 to squeeze in something bigger. The naming is decoded in Quantization explained.
How fast will Qwen3 8B run on the RTX 4060?
We haven't measured this card ourselves. Community reports for 7–8B dense models at Q4 on the RTX 4060 cluster around 41–51 tokens/sec. TurboLLM measures the real number on your machine the first time you load the model.
What's the biggest model an RTX 4060 can run?
Fully in VRAM: about a 9B dense model at Q4. With expert offload: Qwen3.6 35B-A3B at Q3_K_M (16.6 GB on disk), with its experts in system RAM and attention plus the KV cache on the GPU.
Do I need to compile anything?
No. npx turbollm detects the RTX 4060 and downloads a prebuilt CUDA engine automatically — nothing to compile, no Python environment. See how engines work.