Can I run Qwen-AgentWorld-35B-A3B on an RTX 5080 (16GB)?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-07-10.
Memory breakdown (Q4_K_M, F16 KV, 33K context)
| Model weights | 19.8 GB |
| KV cache | 0.6 GB |
| Runtime overhead + reserve | 5.4 GB |
| Total used | 25.8 / 16 GB |
| Short by | 9.8 GB |
Max context that fits at Q4_K_M: does not fit.
Every quantization on the RTX 5080
| Weight quant | Weights | Fits (KV F16) | Used @32K |
|---|---|---|---|
| Q4_K_M | 19.8 GB | ❌ won't fit | 25.8 / 16.0 GB |
| Q5_K_M | 23.0 GB | ❌ won't fit | 29.5 / 16.0 GB |
| Q6_K | 26.5 GB | ❌ won't fit | 33.4 / 16.0 GB |
| Q8_0 | 34.3 GB | ❌ won't fit | 42.2 / 16.0 GB |
| FP16 | 64.6 GB | ❌ won't fit | 76.1 / 16.0 GB |
Lower weight quants free memory at some output-quality cost — Q4 is the common sweet spot; below that quality drops faster.
KV cache is F16 here (llama.cpp default). Drop it to Q8/Q4 (-ctk/-ctv) for more context.
Embed this verdict
Live badge for your README or model card — recomputed by the engine, never stale:
[](https://fitllm.run/can-i-run/qwen-agentworld-35b-a3b-on-rtx-5080)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm "Qwen-AgentWorld-35B-A3B" --gpu "RTX 5080"Why most VRAM calculators get this wrong
Qwen-AgentWorld-35B-A3B is a hybrid model: only 10 of its 40 layers use full attention — the rest are linear and keep no growing KV cache. Naive calculators count every layer at full context and badly over-estimate.
What fits on the RTX 5080 instead
same GPU Models that fit on the RTX 5080: Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b, Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it.
same model GPUs that run Qwen-AgentWorld-35B-A3B: RTX 5090 (32GB), RTX 6000 Ada (48GB), RTX PRO 6000 Blackwell (96GB), Radeon PRO W7900 (48GB), 2× RTX 3090 (48GB), 2× RTX 4090 (48GB), 4× RTX 3090 (96GB), A100 40GB (40GB), A100 80GB (80GB), H100 80GB (80GB), H200 141GB (141GB), B200 (192GB).
Reproduce it
Qwen-AgentWorld-35B-A3B = 34.7B (3B active, MoE), 40 layers. The RTX 5080 has 16GB / 960GB/s. Same math, open source: fitllm-engine. GGUF bpw from llama.cpp.
All numbers are computed by the open-source fitllm-engine (MIT) from official model config.json values — reproduce or audit them yourself. Estimates; real usage varies with runtime (llama.cpp / MLX / Ollama), driver and display. Found a mismatch? Report it. · FitLLM home