HARDWARE FIT CHECK โข Qwen 3.8 27B Instruct + 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)21.93 GB / 48 GB VRAM
Can I Run Qwen 3.8 27B Instruct on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
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 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (48 GB).
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Runs 100% in GPU VRAM (Optimal Headroom)21.93 GB (46%)~34 t/s
Qwen 3.8 27B Instruct on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
โ Fits Comfortably in VRAM
โ FITS IN VRAM
Required VRAM
21.93 GB
46% of hardware capacity (48 GB)Est. Decode Speed
~34 tok/s
Dense model @ 936 GB/sAvailable Headroom
+26.07 GB
Safe for OS display & full contextMemory Allocation & Headroom
21.93 GB allocated of 48 GB capacity
46% utilized
Weights: 16.32 GB
KV: (32K): 4.29 GB
Overhead: 1.32 GB
Free Headroom: +26.07 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~34 tok/s) is memory-bandwidth bound at batch size = 1 on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (936 GB/s). Prompt prefill / TTFT (~493 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen3.8:27bCloud 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:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 54.4 GB | 4.29 GB | 61.34 GB (128%) | ~2 t/s | PARTIAL |
| Q8_08.5 bpw | 28.83 GB | 4.29 GB | 34.88 GB (73%) | ~19 t/s | FITS |
| Q6_K6.56 bpw | 22.3 GB | 4.29 GB | 28.12 GB (59%) | ~25 t/s | FITS |
| Q5_K_M5.68 bpw | 19.31 GB | 4.29 GB | 25.03 GB (52%) | ~29 t/s | FITS |
| Q4_K_M4.8 bpw | 16.32 GB | 4.29 GB | 21.93 GB (46%) | ~34 t/s | FITS |
| FP44.25 bpw | 14.42 GB | 4.29 GB | 19.96 GB (42%) | ~38 t/s | FITS |
| IQ3_XS3.3 bpw | 11.15 GB | 4.29 GB | 16.58 GB (35%) | ~48 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesMulti-GPU Rig48 GB VRAM
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
936 GB/s700W TDP
$1,550Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Consumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8Bโ14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime โ
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