What is the best local LLM for an RTX 5080?

gpt-oss 20B at Q4_K_M is the best OpenClaw pick — about 12-13 GB and ~40 tokens/sec thanks to the 5080's GDDR7 bandwidth. For the highest small-model quality, Qwen 3.5 9B at Q8 runs very fast.

Can the RTX 5080 run Qwen 3.6 27B?

Only at Q3_K_S (~14 GB), which fits but loses code accuracy. Qwen 3.6 27B at the recommended Q4_K_M needs ~17-18 GB, over the 5080's 16 GB. For 27B at a good quant, choose a 24 GB or 32 GB card.

RTX 5080 vs 5090 for local LLMs?

The 5090's 32 GB is the difference-maker: it fits Qwen 3.6 27B at Q6 and gives real headroom for context and larger MoE models, while the 16 GB 5080 tops out at 20B-class models. The 5080 is faster than older 16 GB cards but same fit class; buy the 5090 if VRAM is your bottleneck.

Is 16 GB enough for OpenClaw?

Yes, for gpt-oss 20B agent workloads at Q4. 16 GB comfortably runs a 20B model with clean tool calling, which is enough for most OpenClaw automations. You only need more VRAM if you want 27B-class models or long-context 70B work.

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Hardware July 7, 2026

Best Local LLM for RTX 5080 (2026): 16GB Blackwell VRAM Picks

The RTX 5080 pairs 16 GB of VRAM with fast GDDR7 (~960 GB/s). That bandwidth makes it one of the quickest 16 GB cards for local LLMs — but 16 GB still caps which models load.

Picking hardware for an OpenClaw host?

Use the local model calculator first, then see our AI training options if you want help matching your workload to the right rig.

Short answer: the RTX 5080 is a fast 16 GB card — run gpt-oss 20B at Q4_K_M (~40 tok/sec, clean tool calls) for OpenClaw, or Qwen 3.5 9B at Q8 for the best small-model quality. Its speed is excellent, but it will not fit Qwen 3.6 27B at a good quant; that is a 24 GB job.

The VRAM Math

The 5080 is the “fast 16 GB” pick: near-960 GB/s bandwidth pushes 20B-class tokens/sec well above older 16 GB cards. If you need 27B at a good quant, the 32 GB 5090 is the honest next step.

What Actually Fits (Model Picks)

Model	Quant	VRAM used	Speed	Notes
gpt-oss 20B	Q4_K_M	~12-13 GB	~40 tok/s	Best OpenClaw agent pick
Qwen 3.5 9B	Q8_0	~10 GB	~55 tok/s	Fastest max-quality small
Qwen 3.6 27B	Q3_K_S	~14 GB tight	~30 tok/s	Fits, but Q3 loses code accuracy
Llama 3.1 8B	Q8_0	~9 GB	~60 tok/s	Snappy general chat

What You Can’t Run

Qwen 3.6 27B at Q4_K_M+ — needs ~17-18 GB; the 5080’s speed cannot make 16 GB hold an 18 GB model.
70B at a usable quant — not on a single 16 GB card.
Big MoE models (100B+) — these want far more memory; use unified-memory Macs or a workstation card.

🎮 FAST 16 GB — OR 32 GB+ FOR 27B AND UP

For a linkable 16 GB card the 4070 Ti Super is the same fit class as the 5080. To run Qwen 27B at a good quant or 70B at long context, step to the 32 GB 5090 or the 96 GB Blackwell.

4070TiSGIGABYTE RTX 4070 Ti Super 16 GB ↗ 5090GIGABYTE RTX 5090 32 GB ↗ 96GBRTX PRO 6000 Blackwell 96 GB ↗

OpenClaw Setup

Point OpenClaw at your local model through Ollama:

# pull and run your pick, then set it as the OpenClaw default
ollama pull gpt-oss:20b
openclaw config set agents.defaults.models.chat "ollama/gpt-oss:20b"

For agent reliability, prefer a model with clean tool-call output (gpt-oss 20B where it fits) and cap context to what your memory holds. See the tool-calling reliability guide.