Can you run Llama 3.1 8B Instruct on RTX 5080?
Llama 3.1 8B Instruct Q4_K_M on the NVIDIA GeForce RTX 5080: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 5080 runs Llama 3.1 8B Instruct. 6.5 GB weights at Q4_K_M vs 14.4 GB usable VRAM; ~94 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 5080, https://modelfit.io/can-i-run/llama3.1-8b-q4-on-rtx-5080/, 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 5080, in order of what usually breaks first.
Where the RTX 5080 sits for Llama 3.1 8B Instruct
On the RTX 5080, Llama 3.1 8B Instruct runs with room to spare (~94 tok/s est.). The cheapest card that also runs it comfortably is the RTX 4060 (8 GB, ~30 tok/s est.). Context ceiling on this card: 32k; on the RTX 4060: none.
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: 14.4 GB.
Workload verdicts
Llama 3.1 8B Instruct on the RTX 5080, 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.
The model leaves about 5.9 GB of the usable VRAM budget free after weights and 16k 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 ~0.4s (instant prefill), then ~94 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 5080: FAQ
Can the NVIDIA GeForce RTX 5080 run Llama 3.1 8B Instruct?
Yes. Llama 3.1 8B Instruct (Q4_K_M) loads in about 6.5 GB and the RTX 5080 offers 14.4 GB of usable VRAM, leaving headroom for context. Expect at roughly 94 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 5080 budget is 14.4 GB (16 GB x 90%).
What is the best quantization of Llama 3.1 8B Instruct for the RTX 5080?
The Q5_K_M build is the highest quality that fits (8 GB vs 14.4 GB usable). The Q4_K_M build at 6.5 GB leaves more room for long context.
What GPU do I need to run Llama 3.1 8B Instruct comfortably?
The RTX 5080 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 5080?
Up to 32k tokens stay fully in VRAM (10.5 GB total with the KV cache). At 64k tokens the total reaches 14.5 GB and tips over the budget. Quantizing the KV cache to q8_0 roughly halves the KV column and buys back about one context tier.
Is Llama 3.1 8B Instruct on the RTX 5080 fast enough for coding agents?
Barely on paper: the engine estimates ~94 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.