Can you run Llama 3.1 8B Instruct on RTX 4060?
Llama 3.1 8B Instruct Q4_K_M on the NVIDIA GeForce RTX 4060: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 4060 runs Llama 3.1 8B Instruct. 6.5 GB weights at Q4_K_M vs 7.2 GB usable VRAM; ~30 tok/s est. (ModelFit, 2026).
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Llama 3.1 8B Instruct on RTX 4060, https://modelfit.io/can-i-run/llama3.1-8b-q4-on-rtx-4060/, updated September 2026, CC BY 4.0.
Last updated: September 24, 2026 · Editor: ModelFit Team
What limits this combo
The four constraints the fit engine checks for Llama 3.1 8B Instruct on the RTX 4060, in order of what usually breaks first.
Where the RTX 4060 sits for Llama 3.1 8B Instruct
The RTX 4060 is the cheapest tracked card that runs Llama 3.1 8B Instruct fully in VRAM (~30 tok/s est.). Every cheaper card in the table forces partial offload. The next step up, the RTX 3060, reaches ~42 tok/s est. with a 32k context ceiling.
Same engine as the verdict above: 90% of VRAM usable, KV-cache at fp16, bandwidth-derived tok/s. Verdicts of the other cards are computed for Llama 3.1 8B Instruct Q4_K_M exactly.
Quant explorer
Switch between the quality-gated builds tracked for this exact combo. Q4_K_M stays the default recommendation; heavier Q6/Q8 builds only appear when their exact Ollama tags are registry-verified.
Why no 2-bit builds: Q1/Q2-class quants can look attractive in a memory table, but their quality loss is large enough that ModelFit excludes them from rankings instead of inflating the catalog with junk options. Usable VRAM: 7.2 GB.
Workload verdicts
Llama 3.1 8B Instruct on the RTX 4060, graded per use case from the model's registry-verified tuning and the engine's speed estimate for this exact combo.
Grades combine tag-verified tuning (a model not built for the workload caps at C) with the tok/s floor each workload needs to feel usable. A combo that partially offloads caps at C; one that does not fit is D everywhere.
Memory math: weights + context vs budget
Weights take 6.5 GB. Context costs extra KV-cache on top; this is where long-context sessions break on cards that technically fit the weights.
This configuration exceeds the comfortable VRAM budget by about 1.3 GB. Expect offload pressure or a shorter safe context.
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.
See how fast it feels
A deterministic typing simulation for Llama 3.1 8B Instruct: first token ~0.5s (instant prefill), then ~30 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Llama 3.1 8B Instruct on RTX 4060: FAQ
Can the NVIDIA GeForce RTX 4060 run Llama 3.1 8B Instruct?
Yes. Llama 3.1 8B Instruct (Q4_K_M) loads in about 6.5 GB and the RTX 4060 offers 7.2 GB of usable VRAM, leaving headroom for context. Expect at roughly 30 tokens/sec (est.).
How much VRAM does Llama 3.1 8B Instruct need?
About 6.5 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 2.0 GB extra at 16k tokens. The RTX 4060 budget is 7.2 GB (8 GB x 90%).
What is the best quantization of Llama 3.1 8B Instruct for the RTX 4060?
Stick with the Q4_K_M build at 6.5 GB; every heavier quant exceeds the 7.2 GB usable VRAM.
What GPU do I need to run Llama 3.1 8B Instruct comfortably?
The RTX 4060 already runs Llama 3.1 8B Instruct comfortably. Larger cards only buy you longer context or a heavier quant.
How much context can Llama 3.1 8B Instruct use on the RTX 4060?
None worth having: the 6.5 GB of weights alone exceed the 7.2 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is Llama 3.1 8B Instruct on the RTX 4060 fast enough for coding agents?
Barely on paper: the engine estimates ~30 tok/s against the ~30 tok/s an agentic loop needs to feel responsive, but Llama 3.1 8B Instruct is not tuned for tool-calling, which caps its agentic grade; a coding-tuned model is the better pick here.