Can I Run Qwen 3.8 27B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 3.8 27B Instruct (27.2B) on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (24 GB).
• Tight Fit — Runs 100% on GPU (Low Headroom)
Local Hardware Configurations & Pricing
Tight Fit: Consider 32GB+ for Safe HeadroomNVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0
NVIDIA GeForce RTX 3090 (24GB GDDR6X)
The #1 budget 24GB card for local LLMs, running 14B–32B models and Q4 70B quantized
Cloud Instance Options for Qwen 3.8 27B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 54.4 GB | 4.29 GB | 61.34 GB (256%) | 0 t/s | OOM |
| Q8_08.5 bpw | 28.83 GB | 4.29 GB | 34.88 GB (145%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 22.3 GB | 4.29 GB | 28.12 GB (117%) | ~6 t/s | PARTIAL |
| Q5_K_M5.68 bpw | 19.31 GB | 4.29 GB | 25.03 GB (104%) | ~7 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 16.32 GB | 4.29 GB | 21.93 GB (91%) | ~35 t/s | FITS |
| FP44.25 bpw | 14.42 GB | 4.29 GB | 19.96 GB (83%) | ~39 t/s | FITS |
| IQ3_XS3.3 bpw | 11.15 GB | 4.29 GB | 16.58 GB (69%) | ~50 t/s | FITS |
Frequently Asked Questions: Qwen 3.8 27B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)
How much VRAM does Qwen 3.8 27B Instruct need on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 3.8 27B Instruct requires 21.93 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Qwen 3.8 27B Instruct achieve on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Based on NVIDIA GeForce RTX 3090 (24GB GDDR6X)'s 936 GB/s memory bandwidth, expected decode speed is approximately 35 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.